Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

Abstract

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.

1. Introduction

Metaheuristics are stochastic search algorithms inspired by the natural phenomenon of biological evolution and human behaviors. Metaheuristics (MH) are simple, flexible, and derivative-free techniques that can efficiently solve various challenging, non-convex optimization problems. The meta-heuristic algorithm solves the optimization problem by considering only the input and output of the base system defined as an optimization problem and does not require a strict mathematical model. Therefore, metaheuristic algorithms are very successful in solving real-world optimization problems with complex information [1,2,3].

The meta-heuristic techniques can be divided into two main classes: evolutionary algorithms (EA) and swarm intelligence (SI) algorithms. EAs are based on the theory of evolution and natural selection, which is a way of explaining how species change over time. Examples of classical evolutionary computing techniques are genetic algorithm (GA), evolution strategy (ES), simulated annealing (SA), differential evolution (DE), and genetic programming (GP) [4,5]. SI algorithms mimic social interactions in nature (such as animals, birds, and insects) [6]. Some of the well-known SI methods include particle swarm optimization (PSO), salp swarm optimization (SSA) [7], ant colony optimization (ACO) [8], firefly algorithm (FA), and whale optimization algorithm (WOA) [9].

There has been a sudden rise in the popularity of machine learning-based techniques within the past few decades. The performance of many areas, such as self-driving vehicles, medical, industrial manufacturing, image recognition, etc., has been greatly improved because of machine learning [10]. Machine learning is a computational process, and its main objective is to recognize different patterns in a relatively random set of data. Several machine learning methods like logical regression, naïve Bayes (NB), k-nearest neighbors (k-NN), decision trees, artificial neural network (ANN), and support vector machines (SVM) [11,12,13] are available in the literature for solving various classification and regression problems.

ANNs are one of the most famous and practical machine learning methods developed to solve complex classification and regression problems. An ANN is a computational model which is biologically inspired. It consists of neurons, which are the processing elements, and the connections between them, which have some values known as weights [14]. Neurons are usually connected by these weighted links over which information can be processed. The main characteristics of a neural network are learning and adaptation, robustness, storage of information, and information processing [15]. ANNs are widely used in areas where traditional analytical models fail because either the data is not precise or the relationship between different elements is very complex. ANNs are used in pattern recognition [16,17,18], signal processing [19], intelligent control [20], and fault detection in electrical power systems [21,22,23]. Therefore, by applying ANNs to different fields, we can optimize the performance, and this has been a major research focus for the last few years.

The performance of a neural network depends upon the construction of the network, the algorithms used for training, and the choice of the parameters involved. Usually, gradient-based methods like backpropagation, gradient descent, Levenberg Marquardt back propagation (LM), and scaled conjugate gradient (SCG) are used to train neural networks. However, these classical methods are highly dependent on initial solutions and may trap in the local minima resulting in performance degradation [24]. Therefore, to enhance performance, evolutionary computing techniques can be applied to train neural networks. These networks are known as Evolutionary Neural Networks.

With the rapid development in computing techniques, a variety of metaheuristic search methods have been used for the optimal training of neural networks [24,25,26]. The basic inspiration for these metaheuristic search methods is the natural phenomenon of biological evolution and human behaviors. Metaheuristic algorithms consider different parameters of ANN as an optimization model and then make an effort to find a near-optimal solution [27]. Due to the added benefit of the powerful global and local searching capabilities of metaheuristic algorithms, the hybrid ANN is becoming a great tool for solving different classification and regression problems.

1.1. Contributions and Organization

According to the no free lunch theorem (NFL) [28], no metaheuristic algorithm can efficiently solve all optimization problems. An algorithm that gives excellent results for one optimization problem may perform poorly when applied to another optimization problem. This inequality was the motivation for this research. The main work of this paper was based on the refinement of the biologically inspired reptile search algorithm (RSA) [29]. In an extensive application, we applied this algorithm to solving various regression and classification problems.

RSA is a newly developed optimization technique that can efficiently solve various optimization problems. However, when applied to highly multidimensional nonconvex optimization problems like neural network training, the algorithm shows prominent shortcomings, such as stagnation at local minima, slow convergence speed, and high computational complexity. Therefore, in this paper, some improvements are proposed to make up for the above-mentioned drawbacks.

The reason for local minima stagnation is the lack of exploration in the high-walking stage of the RSA algorithm. Local minima trapping can be avoided if the solution candidates explore the search space as widely as possible. Therefore, to enhance exploration, a sine operator was included in the high walking stage of the RSA algorithm. The modified sine operator with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space.

The second innovation was designed to enhance the convergence capabilities in the hunting phase of the RSA algorithm. The Levy flight operator with jump size control factor $\zeta$ was used to increase the exploitation capabilities of the search agents. The lower value of $\zeta$ results in small random steps in the hunting phase of IRSA. This enables the solution candidates to search the area nearest to the obtained solution, which greatly improves the convergence capabilities of the algorithm.

As compared to the other state-of-the-art optimization algorithms, the computational complexity of RSA is very high. This time complexity limits the application of the algorithm in solving high-dimensional complex optimization algorithms. In the original RSA, the major causes of the complexity are the hunting operator ${\mu }_{\left(j,k\right)}$ and the reduce function $R_{\left(j,k\right)}$. Especially while computing $R_{\left(j,k\right)}$, division by a small value, $ϵ$, greatly increases the computational time of the algorithm. However, in the proposed algorithm with the above-mentioned improvisation, there was no need to calculate these complex operators, and they were excluded from the algorithm. This results in an almost 3-to-4-fold reduction in time complexity.

Thus, the proposed novel IRSA algorithm gives two benefits. First, there was a 10 to 15% improved performance as compared to the original RSA (experimentally verified in Section 3.2). Second, there was a 3-to-4-fold reduction in time complexity as compared to the original RSA (experimentally verified in Section 3.3). The major contributions of the research are:

• An improved reptile search algorithm (IRSA) based on a sine operator and Levy flight was proposed to enhance the performance of the original RSA.

• The proposed IRSA was evaluated using 23 benchmark test functions. Various qualitative, quantitative, comparative, statistical, and complexity analyses were performed to validate the positive effects of the improvisations.

• This research also proposed a hybrid methodology that integrates Multi-Layer Perceptron Neural Network with the improvised RSA for solving various classification problems.

• Finally, the IRSA was applied to train a radial basis function neural network (RBFNN) for short-term wind and solar power predictions.

The remaining article is assembled into seven sections. The proposed methodology is explained in Section 3. Section 4 delineates the effectiveness of the proposed IRSA through the performance of various experimental and statistical studies, along with comparisons. Section 4 also gives a brief rundown on the theoretical basis of the multi-layer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), and proposed improved reptile search algorithm based neural network (IRSANN). Section 5 and Section 6 describe applications of the proposed technique in solving real-world classification and regression problems. Finally, Section 7 concludes the research.

1.2. Literature Survey

In the literature, various metaheuristic optimization techniques have been proposed and investigated for the training of the artificial neural network. The research in [30] used a hybrid AI model to predict the speed of wind at different coastal locations where PSO was applied to train an ANN. A comparison was made between the support vector machine, ANN, and hybrid ANN based wind speed prediction models, and it was observed that the root mean square error (RMSE) was the minimum for the hybrid PSOANN model. In [31], a hybrid ANN-PSO model was used to extract maximum power from PV systems. Different test scenarios were considered, and it was observed that the maximum power was tracked by the ANN-PSO technique. The research in [32] explored a hybridized ANN-BPSO (binary PSO) model to control renewable energy resources in a virtual power plant. Simulation results clearly showed that the best energy management schedule was obtained by the hybrid ANN-BPSO algorithm. The work in [33] developed a hybrid model of an ANN and ant lion optimization (ALO) algorithm for the prediction of suspended sediment load (SSL). In [34], a new hybrid intelligent artificial neural network (NHIANN) with a cuckoo search algorithm (CS) was proposed to develop a forecasting model of criminal-related factors. Based on different performance parameters like mean absolute percentage error (MAPE), it was observed that not only did CS-NIHANN train the model faster but also obtained the optimal global solution.

In recent years, the trend of combining various metaheuristic techniques for improved performance has risen, and many cooperative metaheuristics techniques have been proposed in the literature [35]. To predict energy demand, the genetic algorithm and particle swarm optimization were combined in [36]. Different parameters, like electricity consumption per capita, income growth rate, etc., were used as input for the hybrid prediction model. It was concluded that the performance of the ANN-GA-PSO based model was better than the ANN-GA or ANN-PSO models. In [37], a modified butterfly optimization (MBO) position updating mechanism was improved by utilizing the exploitation capabilities of the multi-verse optimizer (MVO). The work in [38] combined the exploration capabilities of a sine cosine algorithm (SCA) with a dynamic group-based cooperative optimization algorithm (DGCO) for efficient training of a radial basis function neural network. In [39], the performance of a salp swarm algorithm was greatly improved by integrating Levy flight and sine cosine operators. In [40], an improved Jaya algorithm was proposed, which used Levy flight to provide a perfect balance between exploration and exploitation.

2. Proposed Methodology

2.1. Reptile Search Algorithm

The reptile search algorithm is a metaheuristic technique inspired by the hunting behaviors of crocodiles in nature [29]. The working of the RSA depends upon two phases: the encircling phase and the hunting phase. The RSA switches between the encircling phase and the hunting search phase, and the shifting between different phases is performed by dividing the number of iterations into four parts.

2.1.1. Initialization

The reptile search algorithm starts by generating a set of initial solution candidates stochastically using the following equation:

(1)$x_{jk}=rand\times \left(U_b-L_b\right)+L_{b}k=1,2,\dots n$

where $x_{jk}$ = initialization matrix, j = 1,2…. P. P represents population size (rows of the initialization matrix), and n represents dimensions (columns of the initialization matrix) of the given optimization problem. $L_b$, $U_b$, and $rand$ represent the lower bound limit, upper bound limit, and randomly generated values.

2.1.2. Encircling Phase (Exploration)

The encircling phase is essentially an exploration of a high-density area. In the course of the encircling phase, high walking and belly walking, which are based on crocodile movements, play a very important role. These movements do not help in catching prey but help in discovering a wide search space.

(2)$x_{jk}\left(\tau +1\right)=Bes{t}_k\left(\tau \right)\times \left(-{\mu }_{\left(jk\right)}\left(\tau \right)\right)\times \beta -\left(R_{\left(jk\right)}\left(\tau \right)\times rand\right),\tau \le \frac{T}{4}$

(3)$x_{jk}\left(\tau +1\right)=Bes{t}_k\left(\tau \right)\times x_{\left(r_1,k\right)}\times ES\left(\tau \right)\times rand,\tau \le 2\frac{T}{4}\mathrm{and}\tau >\frac{T}{4}$

where $Bes{t}_k\left(\tau \right)$ is the optimal solution obtained at the $k{\mathrm{th}}_{}$ position, rand represents a random number, $\tau$ shows the present iteration number, and the maximum number of iterations is represented by T. ${\mu }_{\left(j,k\right)}$ is the value of the hunting operator of the $j{\mathrm{th}}_{}$ solution at the $k{\mathrm{th}}_{}$ position. The value of ${\mu }_{\left(j,k\right)}$ is determined as shown below:

(4)${\mu }_{\left(j,k\right)}=Bes{t}_k\left(\tau \right)\times P_{\left(j,k\right)}$

where $\beta$ is a sensitivity parameter, and it explains the exploration accuracy. Another function named $R_{\left(j,k\right)}$, whose purpose is to reduce the search space area, can be calculated as follows:

(5)$R_{\left(j,k\right)}=\frac{Bes{t}_k\left(\tau \right)-P_{\left(r_2,k\right)}}{Bes{t}_k\left(\tau \right)+ϵ}$

where $r_1$ is the value of the random number that lies between 1 and N. Here, N represents the total number of candidate solutions. $z_{\left(r1,l\right)}$ represents a random position for the $k{\mathrm{th}}_{}$ solution. $r_2$ is also an arbitrary (random) number ranging between 1 and N, while $ϵ$ represents a value of a small magnitude. $ES\left(\tau \right)$, known as Evolutionary Sense, is a probability-based ratio. The Evolutionary Sense can be mathematically represented as follows:

(6)$ES\left(\tau \right)=2\times r_3\times \left(1-\frac{1}{T}\right)$

where $r_3$ represents a random number. $P_{\left(j,k\right)}$ can be computed as:

(7)$P_{\left(j,k\right)}=\alpha +\frac{x_{\left(j,k\right)-M\left(x_j\right)}}{Bes{t}_k\left(\tau \right)x(U_{b\left(k\right)}-\left(L_{b\left(k\right)}\right)+ϵ}$

where $\alpha$ is a sensitivity limit that controls the exploration accuracy.$M\left(x_j\right)$ is the average position of the $j_{th}$ solution and can be calculated as:

(8)$M\left(x_j\right)=\frac{1}{n}{{\displaystyle \sum }}_{k=1}^n{x}_{\left(j,k\right)}$

2.1.3. Hunting Phase (Exploitation)

The hunting phase, like the encircling phase, has two strategies, namely hunting coordination and cooperation. Both these strategies are used to traverse the search space locally and help target the prey (find an optimum solution). The hunting phase is also divided into two portions based on the iteration. The hunting coordination strategy is conducted for iterations ranging from $\tau \le$ 3$\frac{T}{4}$ and $>2$ $\frac{T}{4}$, while the hunting cooperation is conducted from $\tau \le T$ and $\tau >3$ $\frac{T}{4}$. Stochastic coefficients are used to traverse the local search space to generate optimal solutions. Equations (9) and (10) are used for the exploitation phase:

(9)$x\left(j,k\right)\left(\tau +1\right)=Bes{t}_k\left(\tau \right)\times (P_{\left(j,k\right)}\left(\tau \right))\times rand,\tau \le 3\frac{T}{4}\mathrm{and}\tau >2\frac{T}{4}$

(10)$x\left(j,k\right)\left(\tau +1\right)=Bes{t}_k\left(\tau \right)-{\mu }_{\left(j,k\right)}\left(\tau \right)\times ϵ-R_{\left(j,k\right)}\left(\tau \right)\times rand,\tau \le T\mathrm{and}\tau >3\frac{T}{4}$

where $Bes{t}_k\left(\tau \right)$ is the $k$ position in the best-obtained solution in the current iteration. Similarly, ${\mu }_{\left(j,k\right)}$ represents the hunting operator, which is calculated by Equation (4).

2.2. Proposed Improved Reptile Search Algorithm (IRSA)

The RSA is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high dimensional nonconvex optimization problems, the RSA poses some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping [41,42]. Therefore, to overcome these issues, some adjustments are proposed to the original RSA algorithm.

Avoiding local minima trapping requires the solution candidates to explore the search space as widely as possible. Therefore, to enhance exploration, a sine operator was included in the high walking stage of the RSA algorithm. This adjustment was inspired by the dynamic exploration mechanism in the sine cosine algorithm (SCA) [43]. The sine operator provides a global exploration capability. Therefore, the inclusion of a sine operator in the IRSA can avoid local minima trapping by conducting a full-scale search of the solution space. Using the sine operator, in IRSA Equation (2) was replaced with the following equation.

(11)$x_{jk}\left(\tau +1\right)=Bes{t}_k\left(\tau \right)+(r_1\times \sin \left(rand\right)\times |r_2\times Bes{t}_k\left(\tau \right)-x_{jk}|,for\tau \le \frac{T}{3}$

where $r_1$, $r_2$, and rand are randomly chosen numbers between 0 and 1. $x_{jk}$ is the current position, and $Bes{t}_k$ is the best solution. The Levy flight [44] is a random process that follows the Levy distribution function. As suggested by yang [45]:

(12)$levy=0.01\times \frac{u}{v^{\frac{1}{\zeta }}}$

where u and v obey normal distribution.

(13)$u~\left(0,{\sigma }_u^2\right),v~\left(0,{\sigma }_v^2\right)$

(14)${\sigma }_u={\left(\frac{\delta \left(1+\zeta \right)sin\frac{\pi \zeta }{2}}{\delta \left[\frac{1+\zeta }{2}\right]\zeta \ast 2^{\zeta -\frac{1}{2}}}\right)}^{1/\zeta }$

(15)${\sigma }_v=1$

where $\delta$ is the standard gamma function.$\zeta$ is an important parameter in Levy fight that determines jump size. The lower value of $\zeta$ results in small random steps. This enables the solution candidates to search the area nearest to the obtained solution, which improves exploitation capabilities. This improved exploitation guarantees global convergence. Therefore, instead of Equation (10) the Levy operator is used to update the position in the final stages of IRSA

(16)$x_{jk}\left(\tau +1\right)=Bes{t}_k\left(\tau \right)+randn\times levy\oplus (x_{jk}-Bes{t}_k\left(\tau \right)),\mathrm{for}\tau \le T\mathrm{and}\tau >3\frac{T}{4}$

where ⊕ designates entry-wise multiplication, and randn is a uniformly distributed random number. The Levy random walk is represented in Figure 1.

These improvisations greatly reduce the complexity of the algorithm. In the original RSA, Equations (4) and (5) are highly complex and increase the time complexity of the algorithm. However, with the above-mentioned adaptations, we can exclude these equations from the algorithm. This means that the proposed IRSA algorithms do not have to compute these equations, which results in an almost 3-to-4-fold reduction in time complexity.

Improved global exploration, high efficiency, high convergence speed, and low time complexity are the improvements observed in the proposed technique. Taken together, the pseudocode and flowchart of the IRSA are shown in Algorithm 1 and Figure 2.

Algorithm 1 Pseudocode of IRSA

Initialize random population xInitialize iteration counter$\tau$= 0, maximum iteration T, alpha, betawhile$\tau$< TEvaluate fitness of potential candidatesDetermines the best solutionUpdate Es,$P_{\left(j,k\right)}$using Equations (6) and (7)for j = 1: p     for k = 1: n         If $\tau \le \frac{T}{3}$           Solve using Equation (11)         else if $\tau \le 2\frac{T}{4}and\tau >\frac{T}{3}$           Solve using Equation (3)         else if $\tau \le 3\frac{T}{4}and\tau >2\frac{T}{4}$           Solve using Equation (9)         else           Solve using Equation (16)            end if    end for end for t = t + 1 end while Return best solution

3. Experimental Verification Using Benchmark Test Functions

In this section, the improved reptile search algorithm (IRSA) was tested using 23 standard benchmark functions [46]. These functions were unimodal, multimodal, and fixed-dimension minimization problems. The specifications of the test functions are provided in Table A1, Table A2 and Table A3. For further verification, the IRSA results were compared with the RSA algorithm and also with other classical and state-of-the-art metaheuristic techniques, like barnacle mating optimizer (BMO) [47], particle swarm optimization (PSO) [48], grey wolf optimizer (GWO) [49], arithmetic optimization algorithm (AOA) [50], and dynamic group-based cooperative optimization algorithm (DGCO) [51]. The numbers of iterations taken were 500 and 350, with a fixed population size of 50. For IRSA, 𝛼 and 𝛽 were set to 0.1. Furthermore, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, and average values and standard deviation (STD). Lastly, time complexity analysis was performed on CEC-2019 test functions. The simulations were performed using a desktop computer Core i5 with 12 GB RAM. The software used for the simulation was MATLAB (R2018a).

3.1. Qualitative Analysis

Figure 3 gives an indication of different qualitative measures used to evaluate the convergence of the RSA. The first column of each figure indicates the shape of the test functions in two dimensions to depict the topographic anatomy. The second column shows the search history, which depicts the exploration and exploitation behavior of the algorithm. The convergence curve of unimodal functions was fluid and continuous, while for multimodal functions, the curve improved in steps as they were more complex functions. It is clearly visible that the performance of the IRSA in finding an optimum solution improved significantly with an increase in the number of iterations.

3.2. Comparative Analysis

Table 1 and Table 2 show the comparative analysis between the IRSA and other MH algorithms for unimodal, multimodal, and fixed-dimension functions. For unimodal and multimodal functions, each algorithm was evaluated considering population size and iterations of 50 and 500, respectively. For statistical analysis, each algorithm ran 30 times to compute the best value, average value, worst value, and standard deviations [52]. The IRSA showed a great ability to find the optimum value on 16 out of 23 test functions, which represented nearly 70%, and it ranked second among five other test functions. The convergence curves are depicted in Figure 4. These results demonstrate the positive effects of the improvisations in the form of improved global convergence, enhanced optimization accuracy, and increased stability.

Table 3 delineates the performance of the IRSA and other MH techniques at varying dimensions of 30, 100, and 500, considering 300 iterations. From the table, it can be observed that the IRSA provides the best results on 33 out of a total of 39 test evaluations, which represented nearly 84%, and it ranks second of five other evaluations. These results clearly demonstrate the effectiveness of the proposed algorithm in solving high-dimensional complex optimization problems.

3.3. Time Complexity Analysis

Time complexity usually depends on the initialization process, the number of iterations, and the solution update mechanism. In this section, we performed a time complexity analysis of the IRSA to determine the effect of improvisations on the computational time of the algorithm. For this purpose, we used standard benchmark test functions called CEC-2019. Table A4 delineates the specifications of these functions. The following equation is used to determine the complexity of the algorithm [53,54].

(17)$T=\frac{\tilde{T}-T_1}{T_{°}}$

where $T_{°}$ is the computational time of a specific mathematical algorithm. $T_{°}$ was calculated to be 0.09 s; $T_1$ is the computational time of a CEC-2019 test function for 15,000 iterations; $\tilde{T}$ is the mean computational time of the MH technique used to solve CEC-2019 test functions for 10 runs considering 15,000 iterations

These computations were performed using a desktop computer Core i5 with 12 GB RAM considering a population size of 10. Table 4 and Table 5 describe the result of the analysis. The tabular results clearly show that the improvisations greatly reduce the complexity of the RSA algorithm.

4. IRSA for Neural Network Training

4.1. Multi-Layer Perceptron Neural Network (MLPNN)

Multi-Layer Perceptron Neural Network (MLPNN) implementing backpropagation is one of the most widely used neural network models. In MLPNN, the input layer acts as a receiver and provides the received information to the first hidden layer by passing through weights and bias, as shown in Figure 5. The information is then processed by one or multiple hidden layers and is provided to the output layer. The job of the output layers is to provide results by combining all the processed information. The activation signal for each neuron present in the hidden layer is:

(18)$a_j={\displaystyle \sum }_{i=1}^m{w}_{ji}.x_i+b_j$

where $w_{ji}=$ weight coefficient matrix between $m$ input, and $n$ hidden layer neurons and can be written as:

(19)$w_{ji}=\left[\begin{array}{cccc}{w}_{11}& w_{12}& \cdots & w_{1m}\\ w_{21}& w_{22}& \cdots & w_{2m}\\ ⋮& ⋮& \ddots & ⋮\\ w_{j1}& w_{j2}& \cdots & w_{nm}\end{array}\right]$

Assuming the input layer has $m$ neurons, the input vector $x_i$ can be written as:

(20)$x_i=\left[x_1,x_2,\dots \dots .,x_m\right]T$

If the hidden layer has $n$ neurons, then the activation signal vector $A$ can be written as:

(21)$A={\left[a_1,a_2,\dots ,a_j,\dots \dots a_n\right]}^T$

These activation signals are applied to the neuron of the hidden layer. Based on the type of activation function, the active neuron will generate a decision signal $d_j$:

(22)$d_j=\phi \left(a_j\right)$

For the sigmoid function, the decision signal can be calculated as:

(23)$d_j=\frac{1}{1+e^{-a_j}}$

Once decision signals for each neuron in the hidden layers are determined, these signals are multiplied with the output weight coefficient matrix $v_{kj}$ to generate an estimated output as represented by the equation:

(24)$y_k={\displaystyle \sum }_{j=1}^n{v}_{kj}.d_j+b_k$

The purpose of training the MLP neural network is to find the best weight and biases to maximize classification accuracy.

Figure 5

MLP Neural Network.

[Figure omitted. See PDF]

4.2. Radial Basis Function Neural Network (RBFNN)

Radial Basis Function Neural Network is a three-layered universal approximator. The input layer serves as a means to connect with the environment. No computation is performed at this layer. The hidden layer consists of neurons and performs a non-linear transformation using a radial basis function. Essentially, the hidden layer transforms the pattern into higher dimensional space to make it linearly separable. The value of the ith hidden layer neuron can be written as follows:

(25)${\mathsf{\Phi }}_i=e^{\left(-\frac{\left({\left|\left|\overline{X}-{\overline{u}}_i\right|\right|}^2\right)}{2.{\sigma }_i{}^2}\right)}$

where $\overline{X}$ is the input vector, ${\overline{u}}_i$ is the ith neuron’s prototype vector, ${\sigma }_i$ is the ith neuron’s bandwidth, and ${\mathsf{\Phi }}_i$ is the ith neuron’s output.

The output layer performs different linear computations, which is a combination of the input and weight vector. These computations can be represented mathematically as:

(26)$y={\displaystyle \sum }_i^n{\mathrm{w}}_i{\mathsf{\Phi }}_i$

where ${\mathrm{w}}_i$ is the weight connection, ${\mathsf{\Phi }}_i$ is the ith neuron’s output from the hidden layer, and $y$ is the prediction result. The structure of the RBFNN is shown in Figure 6.

4.3. Training of MLPNN and RBFNN Using the Proposed IRSA

The training of parameters, such as weight and biases for MLPNN and the smoothing parameter (σ) for RBFNN, is a highly complex optimization problem. Inefficient training of these parameters results in low classification and prediction accuracy. Usually, gradient-based methods are used to train neural networks. However, these classical methods are highly dependent on initial solutions and may trap in the local minima resulting in performance degradation. Therefore, to minimize the prediction errors, the IRSA was used to determine weight, biases, and σ. Figure 7 shows the flowchart for the proposed IRSANN algorithm.

5. IRSA for Solving Classification Problems

In order to evaluate the performance of the IRSA, eight datasets obtained from [55,56] were used. Table 6 gives a brief description of the datasets. Each dataset was divided into a training set and a testing set. Almost 67% of the data was used for training the ANN, and the remaining 33% was used for testing. Sigmoid was used as an activation function for the hidden layers. Normalized mean squared error, as described by Equation (27), was used as a cost function.

(27)$NRMSE=\frac{1}{\tilde{T}}\sqrt{\frac{1}{N}{\displaystyle \sum }_{i=1}^N{\left(T_i-P_i\right)}^2}$

where $T_i$ and $p_i$ are the true and predicted values. $\tilde{T}$ represents the mean of true value. N represents the total number of data samples. As there is no fixed rule to select the number of neurons, we selected neurons based on the formula [37]:

(28)$h=2\times f+1$

where $f$ represents the number of input features, and h shows the number of selected neurons.

After training the MLP network, different convergence curves for IRSA, RSA, BMO, AOA, and PSO are displayed in Figure 8. The convergence of IRSA is at par with all the other metaheuristic algorithms. On the other hand, the convergence of the classical RSA was average. The accuracy while using the IRSA algorithm was better than the accuracy of other algorithms. The IRSA provided the best results for most of the datasets. Additionally, the low standard deviation of the IRSA was an indication of its strength and stability. The results in Table 7 clearly show that the accuracy of the proposed method is higher than the other four classifiers. Table 8 shows the performance of the comparative techniques in testing the dataset, and Table 9 shows the cost function comparison of all the techniques with best, average, and STD values.

Statistical Indicators for Classification

Precision, recall, and F1 score are the parameters used to evaluate the performance of the different techniques used in this paper. Precision and recall are defined in (29) and (30) respectively:

(29)$Precision=\frac{TP}{TP+FP}$

(30)$Recall=\frac{TP}{TP+FN}$

where TP (true positive), FP (False Positive), and FN (False Negative) are determined by computing the confusion matrix. The ideal value for precision and recall is one. The F1 score can be defined as:

(31)$F1=2\times \frac{Precision\times Recall}{Precision+Recall}$

Table 10 represents the statistical results for the eight selected datasets. The precision, Recall, and $F1$ of the proposed method were higher for six out of the eight datasets. Considering all the parameters mentioned above, we can conclude that the proposed IRSANN method provides optimum results and outperforms all other comparable MH techniques.

6. IRSA for Solving the Regression Problems

Wind and solar energy are amongst the most promising renewable energy sources. However, wind and solar energy production are highly dependent on stochastic weather conditions. This uncertainty makes it challenging to integrate these renewable energy resources with the grid. Accurate energy prediction results in economical market operations, reliable operation planning, and efficient generation scheduling [57]. This demands highly efficient forecasting of wind and solar energy. Therefore, the IRSA is proposed to train a radial basis neural network (RBFNN) for short-term wind and solar power prediction. Normalized mean squared error, as described by equation (27), was used as a cost function. Almost 67% of the data was used for training the ANN, and the remaining 33% was used for testing.

6.1. Wind Power Prediction

For wind power prediction, SCADA systems were used to record the wind speed, wind direction, and power generated by the wind turbine [58,59]. The measurements were taken at 10-min intervals. Figure 8 and Figure 9 show examples of the 48 h-ahead power predictions obtained by the proposed IRSA for both winter and summer seasons. A comparison between true wind power and predicted wind power by all five techniques for the winter and summer seasons is also depicted in Figure 9 and Figure 10. Simulation results clearly indicate the superior prediction capabilities of the proposed technique for both winter and the highly uncertain summer season.

6.2. Solar Power Prediction

The data set used for solar power prediction is available at [60]. The data consisted of two files. The first file contained dc and ac power generation data, and the second file contained sensor readings of ambient temperature, module temperature, and irradiance [61]. For this work, both files were combined using MATLAB code to create a dataset for solar power prediction. The readings were taken at a time interval of 15 min. The input features included ambient temperature, module temperature, and irradiance, while the output feature was the ac power. Figure 11 shows an example of the 48 h-ahead power predictions obtained by using various MH techniques. Simulation results clearly indicate the superior prediction capabilities of the proposed technique.

6.3. Statistical Indicators for Regression

In order to compare the predictive capabilities of the selected models, we used several statistical measures, such as root mean square error (RMSE), relative error (RE), and the coefficient of determination ($R^2$) [38]. These indicators are described by the following equations:

(32)$RMSE=\sqrt{\frac{{{\displaystyle \sum }}_{j=1}^n{\left(y_j-P_j\right)}^2}{n}}$

(33)$RE={\displaystyle \sum }_{j=1}^n\left|\frac{y_j-P_j}{y_j}\right|$

(34)$R^2=1-\frac{{{\displaystyle \sum }}_{j=1}^n{\left(y_j-P_j\right)}^2}{{{\displaystyle \sum }}_{j=1}^n{\left(y_j-\overline{y_j}\right)}^2}$

where $y_j$ and $p_j$ are the true and predicted values, respectively. $\overline{y_j}$ is the mean of the value, and n represents the total number of data samples.

The small values of RMSE and RE and the higher values of $R^2$ give clear indications of the improved accuracy of the proposed model. These values are represented in Table 11. According to this table, the IRSA-RBFNN provides the highest values of $R^2$ and lowest values of RMSE for both wind and solar prediction, proving that the performance of IRSA is best when compared to other prediction models. The PSO-RBFNN model provides a slightly lower prediction efficiency. BMO-RBFN and RSA-RBFN are ranked third and fourth in terms of prediction performance [62,63].

7. Conclusions

The main work of this paper was based on the refinement of the biologically inspired reptile search algorithm (RSA). The main objective was to enhance the exploration phase so that local minima convergence could be avoided. This was achieved by including a sine operator, which avoids local minima trapping by conducting a full-scale search of the solution space. Furthermore, the Levy flight with small steps was introduced to enhance exploitations. These improvisations enhanced not only the performance but also the results via a 3 to 4-fold reduction in the time complexity of the algorithm. The proposed improved reptile search algorithm (IRSA) was tested using various unimodal, multimodal, and fixed-dimension test functions. Finally, as an extensive application, we applied this algorithm to solving real-world classification and regression problems. The model successfully tackled the classification and regression tasks. Statistical, qualitative, quantitative, and computational complexity tests were performed to validate the effectiveness of the proposed improvisations. Based on the results, we can positively conclude that the proposed improvisations are effective in enhancing the performance of the RSA algorithm.

In the future, the IRSA could be explored to train various types of neural networks. Furthermore, applying the IRSA to solve various optimization problems in different domains (i.e., feature selection, maximum power point tracking (MPPT), smart grids, image processing, power control, robotics, etc.) would be a valuable contribution.

Author Contributions

Conceptualization, methodology, software, M.K.K.; validation, formal analysis, M.H.Z.; investigation, resources, S.R.; data curation, visualization, M.M. and S.K.R.M.; supervision, project administration, funding acquisition, F.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. 2-Dimensional Levy random walk along X and Y axis.

Figure 2. Flow chart of IRSA.

Figure 3. Qualitative results for unimodal, multimodal, and fixed-dimension functions.

Figure 4. Convergence curves for F1–F23.

Figure 6. Radial Basis Function Neural Network.

Figure 7. Flow chart of IRSANN algorithm.

Figure 8. Convergence curves for classification.

Figure 9. Wind Prediction (Winter).

Figure 10. Wind Prediction (Summer).

Figure 11. Solar Power Prediction.

Table 1

Results for unimodal and multimodal functions for 50 dimensions, 500 iterations.

Fun	Measure	IRSA	RSA	BMO	PSO	GWO	AOA	DGCO
F1	Best	0	0	6.030e-129	32.4203	4.7922e-35	6.706e-284	1.238e-21
	Worst	0	0	1.961e-121	63.6854	8.0427e-33	1.2838e-114	2.1218e-19
	Average	0	0	2.712e-122	43.2608	1.4545e-33	1.2838e-115	4.7446e-20
	STD	0	0	6.153e-122	9.48589	2.4625e-33	4.0598e-115	7.9075e-20
F2	Best	0	0	1.393e-67	2.35817	1.7406e-20	0	3.7704e-14
	Worst	0	0	7.383e-65	5.32663	9.4593e-20	0	4.68526e-13
	Average	0	0	1.841e-65	3.31147	4.5301e-20	0	1.10333e-13
	STD	0	0	2.663e-65	0.89717	2.2708e-20	0	1.29549e-13
F3	Best	0	0	4.0313e-92	5.5826e+03	4.5340e-04	0.0286	0.0025
	Worst	0	0	2.9026e-76	9.1971e+03	0.0201	0.5838	25.4054
	Average	0	0	1.1183e-76	7.8891e+03	0.0074	0.1907	5.7823
	STD	0	0	1.5330e-76	1.3842e+03	0.0074	0.2277	10.9998
F4	Best	0	0	7.3742e-58	5.43665	7.5397e-09	4.6289e-88	4.97610e-06
	Worst	0	0	2.5295e-52	14.1279	4.4114e-08	0.05287	0.000247
	Average	0	0	4.1512e-53	8.305702	1.8813e-08	0.024560	5.9520e-05
	STD	0	0	8.765e-53	2.59027	1.274e-08	0.02222	7.3133e-05
F5	Best	45.4705	48.9824	48.9417	5.7675e+03	46.1950	48.6070	46.1974
	Worst	47.7889	48.9903	48.9984	1.0132e+04	47.8430	48.9439	47.8660
	Average	46.4656	48.9883	48.9721	8.1573e+03	47.0300	48.7869	47.0569
	STD	0.6506	0.0032	0.0173	2.0349e+03	0.8086	0.1402	0.5917
F6	Best	1.58	11.97	11.39	150.00	2.25	6.43	3.48
	Worst	2.67	12.25	12.44	284.60	2.89	7.44	5.78
	Average	2.37	12.22	11.935	214.76	2.5	6.90	4.67
	STD	0.25	0.0878	0.4067	47.02	0.27	0.34	0.66
F7	Best	7.6084e-06	2.6639e-05	1.5133e-04	0.1347	4.8839e-04	1.6821e-07	0.0014
	Worst	2.8963e-05	6.7811e-05	3.8440e-04	0.2985	0.0037	5.2418e-05	0.0154
	Average	8.7740e-05	9.0710e-05	4.1659e-04	0.2155	0.0022	2.3443e-05	0.0071
	STD	9.3116e-05	5.2933e-05	2.3023e-04	0.0592	0.0012	2.0923e-05	0.0055
F8	Best	−1.0646e+4	−8.845e+3	−5.358e+3	−1.0646e+4	−9.14e+3	−8.0208e+03	−1.0548e+04
	Worst	−6.8637e+3	−4.57e+3	−2.928e+03	−6.8637e+3	−6.13e+3	−6.8298e+03	−7.3571e+03
	Average	−8.7577e+3	−6.04e+3	−3.867e+03	−8.7577e+3	−7.32e+3	−7.2473e+03	−8.5385e+03
	STD	1.0697e+3	1.2009e+3	863.4437	1.0697e+3	902.38	427.9226	849.1905
F9	Best	0	0	0	114.0457	5.6843e-13	0	4.4338e-12
	Worst	0	0	0	140.5094	5.42412	0	1.4080e-09
	Average	0	0	0	131.6173	3.0390	0	4.7419e-10
	STD	0	0	0	15.2178	2.7705	0	8.0872e-10
F10	Best	8.8817e-16	8.8817e-16	8.8817e-16	2.98933	3.9968e-14	8.8817e-16	2.0863e-12
	Worst	8.8817e-16	8.8817e-16	8.8817e-16	4.25557	5.0626e-14	8.8817e-16	20.2605
	Average	8.8817e-16	8.8817e-16	8.881e-16	3.62755	4.3520e-14	8.8817e-16	4.04392
	STD	0	0	0	0.38752	3.3495e-15	0	8.52536
F11	Best	0	0	0	1.18496	0	0.009543	0
	Worst	0	0	0	1.75459	0.014698	0.428251	0.019779
	Average	0	0	0	1.39448	0.00368	0.159273	0.001977
	STD	0	0	0	0.177805	0.006091	0.138820	0.006254
F12	Best	0.0868	1.1349	0.7756	0.1567	0.0373	1.0089	0.1162
	Worst	0.1513	1.4160	1.1959	0.2635	0.0583	1.0286	0.2057
	Average	0.1534	1.3680	1.1688	0.2182	0.0459	1.0395	0.2604
	STD	0.0373	0.1246	0.2121	0.0552	0.0110	0.0316	0.1348
F13	Best	4.5760e-08	1.8359	0.1800	0.1624	0.8989	4.7901	2.8443
	Worst	2.9444	3.8301	0.2521	0.3925	1.6895	4.9737	3.4369
	Average	1.9711	3.0760	0.2071	0.2657	1.1783	4.9183	3.0976
	STD	1.6991	0.7882	0.0293	0.0835	0.3008	0.0811	0.2333

Table 2

Results for fixed-dimension functions considering 500 iterations.

Fun	Measure	IRSA	RSA	BMO	PSO	GWO	AOA	DGCO
F14	Best	0.9980	2.9821	2.0481	1.913	0.9980	7.8740	0.9980
	Worst	2.9834	3.0006	11.7187	2.981	10.7632	12.6705	2.9821
	Average	2.1313	2.9901	6.4665	2.523	5.1025	10.7579	2.1964
	STD	0.8325	0.0139	3.0067	0.7341	4.9092	1.7957	0.6274
F15	Best	0.00030	0.00068	0.00033	0.00038	0.00030	0.00036	0.00035
	Worst	0.01550	0.00896	0.00654	0.00142	0.02036	0.05758	0.001224
	Average	0.00406	0.00180	0.001710	0.000813	0.004417	0.009030	0.000709
	STD	0.00548	0.00253	0.00223	0.00043	0.008408	0.018280	0.000237
F16	Best	−1.03162	−1.03159	−1.03162	−1.03162	−1.03162	−1.03162	−1.03162
	Worst	−1.03162	−0.99552	−0.91896	−1.03162	−1.03162	−1.03162	−1.03162
	Average	−1.03162	−1.02159	−1.01681	−1.03162	−1.03162	−1.03162	−1.03162
	STD	2.8338e-08	0.01150	0.03493	5.4218e-08	1.6501e-08	4.1202e-09	3.1308e-09
F17	Best	0.39788	0.40742	0.39788	0.39788	0.39788	0.39788	0.39788
	Worst	0.397897	1.27099	0.42696	0.39788	0.39788	0.39788	0.39788
	Average	0.397891	0.64371	0.40330	0.39788	0.39788	0.39788	0.39788
	STD	3.8146e-06	0.25782	0.01085	7.2909e-07	4.2567e-07	4.3534e-08	3.6445e-07
F18	Best	3.00000	3.00000	2.99999	3.00000	3.00000	3.00000	3.000000
	Worst	3.000001	3.39511	42.1054	3.00011	3.00002	95.1885	3.000000
	Average	3.000000	3.04555	10.1188	3.00002	3.00001	23.1248	3.00000
	STD	5.1797e-07	0.12303	13.0276	3.477e-05	7.920e-06	28.7189	6.458e-10
F19	Best	−3.86277	−3.85920	−3.85436	−3.86276	−3.86277	−3.86277	−3.86278
	Worst	−3.85374	−3.57053	−1.28898	−3.86201	−3.85315	−1.00081	−3.86277
	Average	−3.86101	−3.75028	−3.44530	−3.86263	−3.86034	−1.85933	−3.86277
	STD	0.00367	0.09664	0.81662	0.00024	0.00403	1.38235	3.6601e-06
F20	Best	−3.32086	−2.89690	−2.45549	−3.32121	−3.20304	−3.32194	−3.32189
	Worst	−3.20101	−0.17365	−0.54213	−3.19851	−3.08259	−3.15098	−3.13200
	Average	−3.27287	−1.54187	−1.24769	−3.22497	−3.17957	−3.27776	−3.21025
	STD	0.06116	1.03850	0.75791	0.05061	0.04241	0.071964	0.06482
F21	Best	−10.1206	−5.0552	−4.9619	−10.2512	−10.0712	−10.1531	−10.1494
	Worst	−5.0552	−5.0552	−1.8717	−5.0552	−5.0552	−5.0552	−5.0549
	Average	−9.0840	−5.0552	−3.8482	−6.0943	−6.0745	−6.0747	−7.0926
	STD	2.2573	3.8458e-07	1.3134	2.7922	2.2793	2.2798	2.7900
F22	Best	−10.3586	−5.08767	−5.05414	−10.4029	−10.4024	−10.4029	−10.3960
	Worst	−3.72361	−5.08766	−2.32919	−3.72365	−5.08765	−3.72429	−3.72380
	Average	−8.44500	−5.08766	−3.83632	−5.87780	−9.33904	−7.60893	−6.66468
	STD	2.83980	1.0352e-06	0.98429	2.449069	2.24067	2.97300	3.25225
F23	Best	−10.52251	−5.128480	−4.99447	−10.53637	−10.5361	−10.5363	−10.5309
	Worst	−3.83461	−5.12847	−2.49710	−3.83472	−5.12845	−2.29603	−3.83496
	Average	−8.48605	−5.12847	−3.77437	−6.62130	−8.91314	−7.34602	−6.50874
	STD	2.94732	2.3686e-06	0.76135	2.73097	2.61167	3.32172	3.45155

Table 3

Results for unimodal and multimodal functions for 30, 100, and 500 dimensions, 300 iterations.

Fun	Dim	IRSA	RSA	BMO	PSO	GWO	AOA	DGCO
F1	30	0	0	2.9729e-75	100.429274	5.806e-20	1.74296e-76	5.93134e-10
	100	0	0	7.502e-46	2951.7571	0.0012668	0.023492033	47.4212010
	500	0	0	5.948e-41	91703.5649	109.12381	0.5862142	113.8580
F2	30	0	0	1.0065e-39	4.59976602	7.467e-11	0	2.10414e-07
	100	0	0	4.6351e-25	53.7327530	0.0134880	4.19883e-60	0.268447521
	500	0	0	3.3724e-23	487.76760	10.64367	0.00161	2.519646
F3	30	0	0	1.1399e-52	1321.79087	0.0001101	6.9638e-104	7.97466788
	100	0	0	3.164e-28	50712.1407	14820.47	1.531983171	80032.5400
	500	0	0	6.6866e-19	1303817.56	513880.05	36.035904	104.83
F4	30	0	0	5.6171e-32	8.65232085	4.478e-05	3.51206e-13	0.04110228
	100	0	0	3.1527e-20	31.480699	5.7402399	0.100756622	82.157639
	500	0	0	5.7108e-18	43.134284	65.76152	0.17991607	0.09866121
F5	30	25.72424	28.99171	28.995762	1300.12057	27.136822	28.59270148	26.21687570
	100	96.8983	98.9901048	98.9698060	226496.089	98.343648	98.90012672	97.5081886
	500	498.3830	499.6902	499.0781	27910234.5	9445.0271	499.1341156	4392.7
F6	30	0.576633	7.0346180	6.97472880	111.668505	0.7535595	3.03144018	1.25053679
	100	11.05309	24.7513018	24.5099804	2069.85282	9.4928888	18.4072873	13.8718994
	500	111.5654	124.751626	123.355451	64419.3854	268.90391	117.2773220	145.94373
F7	30	9.50e-05	1.9397e-05	0.00050276	0.05970424	0.0019118	3.78351e-05	0.00390436
	100	3.97e-05	2.2064e-05	0.0004319	1.2096514	0.0097439	5.96465e-06	0.0300833
	500	8.21e-05	0.00046135	0.00080821	235.665342	0.7073979	0.000119979	0.292147
F8	30	−5214.04	−3431.1934	−2834.3518	−6549.69516	−6277.543	−4918.30201	−5573.32119
	100	−64809.8	−36018.762	−18000.185	−56587.461	−16611.13	−47550.1316	−34933.7702
	500	−80242.2	−44205.61	−12898.155	−36269.793	−58117.96	−22872.6112	−24663.3214
F9	30	0	0	0	103.812346	1.9054417	0	5.86970e-09
	100	0	0	0	587.205492	58.233470	0	20.62413137
	500	0	0	0	4309.97279	902.54104	0	466.7469966
F10	30	8.881e-16	8.8817e-16	8.8817e-16	5.2518904	1.724e-10	8.88178e-16	1.63626e-06
	100	8.881e-16	8.881e-16	8.8817e-16	7.8262077	5.792e-05	8.88178e-16	20.523618
	500	8.881e-16	8.881e-16	4.440e-15	12.927665	2.140639	0.0083857	20.881255
F11	30	0	0	0	1.8485067	0.0298839	0.23855046	1.26669e-10
	100	0	0	0	28.3884325	0.0349668	897.847451	0.04874511
	500	0	0	0	867.60304	3.1100432	13134.6129	162.849911
F12	30	0.048112	1.59700365	0.93494039	0.01698208	0.0712204	0.81438277	0.045133459
	100	0.374642	1.28994163	1.19529976	0.64770498	0.1179354	1.20962184	0.389415151
	500	0.9743	1.2075	1.1980	2.0190	0.5999	1.1800	1.2411
F13	30	1.280496	1.970635	2.6705939	0.15916980	0.1018868	2.9785990	1.600784929
	100	7.876407	9.878585	9.993848	4.57433449	5.5499672	9.963623491	8.44409492
	500	49.99682	49.99734	49.99725	82.318	46.351	49.998	54.177

Table 4

$\tilde{T}\left(sec\right)$ calculation for various metaheuristic algorithms.

Fun	T IRSA	T RSA	T FDO	T BMO	T AOA	T PSO	T GWO	T DGCO	T FPA	T DFA
CEC01	440.04	498.64	9569.9	512.80	468.96	472.38	472.49	472.884	702.73	901.60
CEC02	51.562	158.97	303.77	26.603	9.7899	10.097	11.746	14.144	420.60	858.17
CEC03	66.843	192.89	491.69	35.672	17.950	18.158	19.011	22.222	481.66	1018.4
CEC04	33.761	99.078	514.31	26.605	9.1060	10.075	11.035	12.389	266.56	627.08
CEC05	34.373	99.519	744.31	27.667	9.4832	10.545	11.180	12.966	267.42	491.911
CEC06	175.14	240.27	5176.0	189.23	165.30	169.64	168.52	170.18	423.23	641.59
CEC07	34.831	99.440	384.83	24.768	9.7759	10.604	11.566	12.993	266.66	483.13
CEC08	34.733	99.406	369.56	25.428	9.7797	10.683	11.522	12.848	267.13	481.31
CEC09	33.670	98.69	383.27	23.551	8.3110	9.1362	10.298	11.728	266.52	492.78

Table 5

$T\left(sec\right)$ calculation for various metaheuristic algorithms.

Fun	T 1	$\tilde{\mathit{T}}$ IRSA	$\tilde{\mathit{T}}$ RSA	$\tilde{\mathit{T}}$ FDO	$\tilde{\mathit{T}}$ BMO	$\tilde{\mathit{T}}$ AOA	$\tilde{\mathit{T}}$ PSO	$\tilde{\mathit{T}}$ GWO	$\tilde{\mathit{T}}$ DGCO	$\tilde{\mathit{T}}$ FPA	$\tilde{\mathit{T}}$ DFA
CEC01	0.0105	43.017	48.736	934.10	50.118	45.839	46.173	46.184	46.222	68.655	88.065
CEC02	0.0042	5.101	15.584	29.717	2.665	1.024	1.054	1.215	1.449	41.120	83.826
CEC03	0.0044	6.5924	18.895	48.058	3.5501	1.8205	1.84077	1.9240	2.2374	47.079	99.466
CEC04	0.0031	3.3636	9.7386	50.266	2.6652	0.9572	1.05189	1.1456	1.2777	26.085	61.272
CEC05	0.0035	3.4234	9.7816	72.714	2.7688	0.9940	1.09774	1.1597	1.3340	26.169	48.079
CEC06	0.0068	17.163	23.519	505.25	18.538	16.202	16.6257	16.517	16.679	41.376	62.688
CEC07	0.0044	3.4681	9.7739	37.628	2.4859	1.0226	1.10349	1.1974	1.3367	26.095	47.222
CEC08	0.0045	3.4585	9.7706	36.138	2.5503	1.0230	1.1112	1.1931	1.3225	26.141	47.044
CEC09	0.0036	3.3547	9.7015	37.476	2.3671	0.8796	0.9602	1.0736	1.2132	26.081	48.164

Table 6

Description of used dataset.

Data Set	# Classes	# Features	No. of training samples	No. of testing samples
Iris	3	4	100	50
Heart	2	13	203	100
Stress-Lysis	3	3	1340	661
Banknote-authentication	2	4	919	453
Blood-transfusion	2	4	501	247
Cryotherapy	2	6	60	30
Diabetes	2	8	514	254

Table 7

Training accuracy.

Dataset	Training Accuracy (%)
	PSONN	BMOANN	AOANN	RSANN	IRSANN
Iris	97	94	93	88.02	97
Heart	72.60726	66.9967	59.73597	42.24422	88
Stress-Lysis	91.75412	95.8021	70.16492	59.37031	99.42171
Banknote-authentication	94.42623	97.9235	85.68306	84.48087	99.45355
Blood-transfusion	79.95992	78.15631	77.35471	75.1503	77.55511
Cryotherapy	90	92	71.66667	81.66667	96.66667
Diabetes	74.80469	75	72.07031	66.79688	78.10156
Haberman	75	75	78.43137	79.41176	78.43137

Table 8

Testing accuracy.

Dataset	Testing Accuracy (%)
	PSONN	BMOANN	AOANN	RSANN	IRSANN
Iris	94	98.66	92	91	98.23
Heart	70.9571	75.90759	58.08581	46.53465	85
Stress-Lysis	91.90405	97.0015	70.01499	57.12144	99.7121
Banknote-authentication	93.43545	96.49891	83.3698	85.33917	99.56236
Blood-transfusion	77.51004	77.91165	74.6988	79.11647	80.72289
Cryotherapy	93.33333	86.66667	80	76.66667	93.33333
Diabetes	67.57813	75.78125	76.1718867	69.92188	77.26563
Haberman	67.47059	76.47059	76.64706	70.09804	80.54902

Table 9

Cost evaluation of classification dataset.

Dataset		Cost Function Comparison
		PSONN	BMOANN	AOANN	RSANN	IRSANN
Iris	Best	0.078788	0.082034	0.342085	0.143403	0.072572
	Avg	0.137949	0.087161	0.742686	0.327073	0.115797
	Std	0.083667	0.00569	0.484778	0.159949	0.108159
Heart	Best	0.56952	0.545925	0.995477	0.808033	0.472722
	Avg	0.640322	0.581627	1.372587	0.853887	0.517036
	Std	0.062089	0.030938	0.53151	0.076074	0.038613
Stress-Lysis	Best	0.156856	0.136333	0.321634	0.248556	0.0911631
	Avg	0.159272	0.142592	0.353229	0.627416	0.268016
	Std	0.003418	0.007051	0.027562	0.328421	0.055127
Banknote-authentication	Best	0.318751	0.154256	0.556654	0.503623	0.085457
	Avg	0.331262	0.170783	0.764328	0.836328	0.145555
	Std	0.017693	0.016899	0.207149	0.288424	0.054529
Blood-transfusion	Best	0.823567	0.882403	0.960614	0.958203	0.887161
	Avg	0.830233	0.888582	1.260426	0.976322	0.895859
	Std	0.009426	0.008437	0.259695	0.015692	0.009459
Cryotherapy	Best	0.357101	0.756437	0.234832	0.542086	0.28827
	Avg	0.362329	0.888982	0.292911	0.764291	0.305118
	Std	0.007394	0.129127	0.050999	0.192532	0.127465
Diabetes	Best	1.048615	0.725924	0.730482	0.947266	0.646058
	Avg	1.076385	0.743759	0.7562	0.982109	0.724891
	Std	0.042824	0.019733	0.036372	0.040968	0.07128
Haberman	Best	0.892895	0.906456	0.977251	0.994621	0.881229
	Avg	0.936445	0.931774	1.269601	0.998591	0.925252
	Std	0.061589	0.00719	0.14037	0.003469	0.038883

Table 10

Statistical measures for classification.

Data Set	Technique	Training			Testing
		Precision	Recall	F1 Score	Precision	Recall	F1 Score
Iris	IRSA	0.979142	0.979923	0.979533	0.962222	0.967178	0.969693
	RSA	0.891866	0.875316	0.883513	0.832602	0.796296	0.814045
	BMO	0.944356	0.943915	0.944136	0.977778	0.977778	0.977778
	AOA	0.680918	0.647619	0.663851	0.695926	0.655556	0.675138
	PSO	0.972973	0.969697	0.971332	0.964912	0.958333	0.961612
Heart	IRSA	0.888428	0.884587	0.88503	0.848726	0.851251	0.854971
	RSA	0.462116	0.462022	0.462069	0.60397	0.603725	0.603848
	BMO	0.73858	0.636917	0.683992	0.740629	0.660205	0.698108
	AOA	0.480976	0.481498	0.481237	0.482877	0.486242	0.484554
	PSO	0.657688	0.651564	0.654612	0.602002	0.577957	0.589735
Stress-Lysis	IRSA	0.862953	0.869583	0.866255	0.831595	0.842565	0.837044
	RSA	0.551258	0.515412	0.514234	0.55435	0.508844	0.61734
	BMO	0.959656	0.951436	0.955528	0.975669	0.96373	0.969663
	AOA	0.768447	0.674813	0.718593	0.784879	0.673437	0.7249
	PSO	0.879358	0.895391	0.887302	0.889297	0.905763	0.897455
Banknote-authentication	IRSA	0.995792	0.994563	0.995225	0.995902	0.995349	0.995625
	RSA	0.844413	0.838669	0.841531	0.85288	0.852147	0.852513
	BMO	0.97761	0.98115	0.979377	0.963706	0.967257	0.965478
	AOA	0.57296	0.57642	0.694746	0.809533	0.812795	0.811161
	PSO	0.942964	0.944103	0.943533	0.933375	0.934613	0.933994
Blood-transfusion	IRSA	0.729753	0.595356	0.655739	0.691121	0.570559	0.62508
	RSA	0.627282	0.508097	0.561434	0.730453	0.531909	0.615567
	BMO	0.683964	0.579004	0.627122	0.652637	0.552494	0.598405
	AOA	0.761869	0.511741	0.612243	0.624494	0.505248	0.558578
	PSO	0.739583	0.621758	0.675572	0.686359	0.564815	0.619683
Cryotherapy	IRSA	0.950774	0.943611	0.957148	0.95	0.916667	0.933036
	RSA	0.818449	0.806561	0.812462	0.766667	0.767857	0.767261
	BMO	0.915882	0.928276	0.912064	0.819444	0.830144	0.824759
	AOA	0.699177	0.690236	0.694678	0.638889	0.633333	0.636099
	PSO	0.897321	0.902715	0.90001	0.944444	0.928571	0.936441
Diabetes	IRSA	0.772165	0.7483	0.760045	0.683296	0.676977	0.690122
	RSA	0.626005	0.601743	0.613634	0.66266	0.610454	0.635487
	BMO	0.724919	0.726392	0.725655	0.699808	0.722159	0.710808
	AOA	0.651455	0.614956	0.632679	0.670297	0.643082	0.656407
	PSO	0.704908	0.676813	0.734665	0.765362	0.699496	0.730948
Haberman	IRSA	0.714555	0.561729	0.638992	0.677083	0.54118	0.623362
	RSA	0.519912	0.501389	0.510477	0.650000	0.517637	0.576316
	BMO	0.700376	0.56504	0.625471	0.632979	0.550607	0.588927
	AOA	0.655914	0.574148	0.612313	0.662338	0.58316	0.620232
	PSO	0.674919	0.605186	0.638153	0.662837	0.58249	0.620072

Table 11

Statistical evaluation for wind and solar prediction.

Data Set	Technique	Training			Testing			Cost
		RE	RMSE	R2	RE	RMSE	R2
Wind power prediction (winter)	PSO	0.0157	11.8303	0.9835	0.0886	55.6747	0.9081	0.0121
	RSA	0.0170	16.6071	0.9657	0.1065	61.2748	0.8890	0.0326
	BMO	0.0959	32.5220	0.8278	0.2318	107.9345	0.5259	0.0444
	AOA	0.0356	26.9534	0.8961	0.1135	59.1465	0.8749	0.0373
	IRSA	0.0049	8.3396	0.9918	0.0642	32.7880	0.9665	0.0105
Wind power prediction (summer)	PSO	0.0049	2.8918	0.9907	0.0772	9.5937	0.9780	0.0193
	RSA	0.0055	4.4855	0.9778	0.6042	38.7233	0.6423	0.0251
	BMO	0.0808	5.8582	0.9460	0.2983	22.3706	0.8291	0.0428
	AOA	0.0339	4.0871	0.9779	0.4688	30.6282	0.7565	0.0208
	IRSA	0.0015	2.8416	0.9912	0.0632	9.3401	0.9801	0.0184
PV power prediction	PSO	0.0235	94.9000	0.9649	0.0762	298.7930	0.9420	0.0535
	RSA	0.0144	119.6847	0.9626	0.0440	223.8701	0.9656	0.0410
	BMO	0.0359	148.5061	0.9150	0.1148	232.2480	0.9545	0.0827
	AOA	0.0429	183.5005	0.8987	0.1111	383.4890	0.8913	0.0963
	IRSA	0.0146	90	0.9761	0.1285	260.8531	0.9611	0.0234

Appendix A

Table A1

Utilized Unimodal Test Functions.

Function Description	Dim	Range	${\mathit{f}}_{\mathit{m}\mathit{i}\mathit{n}}$
$f_1\left(x\right)={{\displaystyle \sum }}_{i=1}^n{x}_i{}^2$	500, 100, 50, 30	[−100, 100]	0
$f_2\left(x\right)={{\displaystyle \sum }}_{i=0}^n\left|x_i\right|+{{\displaystyle \prod }}_{i=0}^n\left|x_i\right|$	500, 100, 50, 30	[−10, 10]	0
$f_3\left(x\right)={{\displaystyle \sum }}_{i=1}^d{\left({{\displaystyle \sum }}_{j=1}^i{x}_j\right)}^2$	500, 100, 50, 30	[−100, 100]	0
$f_4\left(x\right)=ma{x}_i\left\{\left|x_i\right|,1\le i\le n\right\}$	500, 100, 50, 30	[−100, 100]	0
$f_5\left(x\right)={{\displaystyle \sum }}_{i=1}^{n-1}\left[100{\left(x_i{}^2-x_{i+1}\right)}^2+{\left(1-x_i\right)}^2\right]$	500, 100, 50, 30	[−30, 30]	0
$f_6\left(x\right)={{\displaystyle \sum }}_{i=1}^n{\left(x_i+0.5\right)}^2$	500, 100, 50, 30	[−100, 100]	0
$f_7\left(x\right)={{\displaystyle \sum }}_{i=0}^{n}i{x}_i{}^4+rand\left[0,1\right]$	500, 100, 50, 30	[−1.28, 1.28]	0

Table A2

Utilized Multimodal Test Functions.

Function Description	Dim	Range	${\mathit{f}}_{\mathit{m}\mathit{i}\mathit{n}}$
$f_8\left(x\right)={{\displaystyle \sum }}_{i=1}^n\left(-x_i\mathit{sin}\left(\sqrt{\lfloor x_i\rfloor }\right)\right)$	500, 100, 50, 30	[−100, 100]	−418.980 × Dim
$f_9\left(x\right)={{\displaystyle \sum }}_{i=1}^n\left[x_i{}^2-10\mathit{cos}\left(2\pi x_i\right)+10\right]$	500, 100, 50, 30	[−10, 10]	0
$$\begin{gathered} f_{10}\left(x\right)=-20e^{\left(-0.2\sqrt{\frac{1}{n}{{\displaystyle \sum }}_{i=1}^n{x}_i{}^2})\right)}-e^{\left(\frac{1}{n}{{\displaystyle \sum }}_{i=1}^n\mathit{cos}\left(2\pi x_i\right)\right)} \\ +20+e \end{gathered}$$	500, 100, 50, 30	[−100, 100]	0
$f_{11}\left(x\right)=1+\frac{1}{4000}{{\displaystyle \sum }}_{i=1}^n{x}_i{}^2-{{\displaystyle \prod }}_{i=1}^n\mathit{cos}\left(\frac{x_i}{\sqrt{i}}\right)$	500, 100, 50, 30	[−100, 100]	0
$$\begin{gathered} f_{12}\left(x\right)=\frac{\pi }{n}\left\{10\mathit{sin}\left(\pi y_i\right)\right\}+ \\ {{\displaystyle \sum }}_{i=1}^{n-1}{\left(y_i-1\right)}^2\left[\begin{array}{c}1+10{\mathit{sin}}^2\left(\pi y_i+1\right)\\ +\\ {{\displaystyle \sum }}_{i=1}^{n-1}u\left(x_i,10,100,4\right)\end{array}\right] \end{gathered}$$                here,             $y_i=1+\frac{x_i+1}{4}$,         $u\left(x_i,a,k,m\right)\left\{\begin{array}{c}K{\left(x_i-a\right)}^{m}if{x}_i>a\\ 0-a\le x_i\ge a\\ K{\left(-x_i-a\right)}^{m}if-a\le x_i\end{array}\right\}$	500, 100, 50, 30	[−30, 30]	0
$$\begin{gathered} f_{13}\left(x\right)=0.1\left({\mathit{sin}}^2\left(3\pi x_i\right) \\ +{{\displaystyle \sum }}_{i=1}^n{(x_i-1)}^2\left[1+{\mathit{sin}}^2\left(\begin{array}{c}3\pi x_i\\ +1\end{array}\right)\right] \\ +{\left(x_n-1\right)}^2\left(1+{\mathit{sin}}^2\left(2\pi x_n\right)\right)\right)+{{\displaystyle \sum }}_{i=1}^{n}u(x_i,5,100,4) \end{gathered}$$	500, 100, 50, 30	[−100, 100]	0

Table A3

Utilized Fixed Dimension Test Functions.

Function Description	Dim	Range	${\mathit{f}}_{\mathit{m}\mathit{i}\mathit{n}}$
$f_{14}\left(x\right)={\left(\frac{1}{500}+{{\displaystyle \sum }}_{j=1}^{25}\left(\frac{1}{j+{{\displaystyle \sum }}_{i=1}^2\left(x_i-a_{ij}\right)}\right)\right)}^{-1}$	2	[−65, 65]	0.998
$f_{15}\left(x\right)={{\displaystyle \sum }}_{i=1}^n{\left[a_i-\frac{x_1\left(b_i{}^2+b_i{x}_2\right)}{b_i{}^2+b_i{x}_3+x_4}\right]}^2$	4	[−1, 1]	0
$$\begin{gathered} f_{16}\left(x\right)=4x_1{}^2-2.1x_1{}^4+\frac{1}{3}{x}_1{}^6+x_1{x}_2-4x_2{}^2 \\ +4x_2{}^4 \end{gathered}$$	2	[−5, 5]	−1.0316
$f_{17}\left(x\right)={\left(x_2-\frac{5.1}{4{\pi }^2}{x}_1{}^6+\frac{5}{\pi }{x}_1-6\right)}^2+10\left(1-\frac{1}{8\pi }\right)\mathit{cos}{x}_1+10$	2	[−4, 4]	0.398
$$\begin{gathered} f_{18}\left(x\right)=\left[1+{\left(x_1+x_2+1\right)}^2\left(19-14x_1+3x_1{}^2-14x_2+6x_1{x}_2+3x_2{}^2\right)\right]\left[30 \\ +{\left(2x_1-3x_2\right)}^2\left(18-32x_1+12x_1{}^2+48x_2-36x_1{x}_2+27x_2{}^2\right)\right] \end{gathered}$$	2	[−5, 5]	3
$f_{19}\left(x\right)=-{{\displaystyle \sum }}_{i=1}^4{c}_i{e}^{\left(-{{\displaystyle \sum }}_{i=1}^3{a}_{ij}{\left(x_j-p_{ij}\right)}^2\right)}$	3	[−5, 5]	−3.86
$f_{20}(x)=-{{\displaystyle \sum }}_{i=1}^4{c}_i{e}^{\left(-{{\displaystyle \sum }}_{i=1}^6{a}_{ij}{\left(x_j-p_{ij}\right)}^2\right)}$	6	[−5, 5]	−1.170
$f_{21}\left(x\right)=-{{\displaystyle \sum }}_{i=1}^5{\left[\left(X-a_i\right){\left(X-a_i\right)}^T+c_i\right]}^{-1}$	4	[−5, 5]	−10.153
$f_{22}\left(x\right)=-{{\displaystyle \sum }}_{i=1}^7{\left[\left(X-a_i\right){\left(X-a_i\right)}^T+c_i\right]}^{-1}$	4	[−5, 5]	−10.4028
$f_{23}\left(x\right)=-{{\displaystyle \sum }}_{i=1}^{10}{\left[\left(X-a_i\right){\left(X-a_i\right)}^T+c_i\right]}^{-1}$	4	[−1, 1]	−10.536

Table A4

Utilized CEC 2019 Test Functions.

Function	Description	${\mathit{f}}_{\mathit{m}\mathit{i}\mathit{n}}$	Range	Dim
CEC-1	Storn’s Chebyshev polynomial fitting problem	1	[−8192, 8192]	9
CEC-2	Inverse Hilbert matrix problem	1	[−16,384, 16,384]	16
CEC-3	Lennard–Jones minimum energy cluster	1	[−4, 4]	18
CEC-4	Rastrigin function	1	[−100, 100]	10
CEC-5	Grienwank function	1	[−100, 100]	10
CEC-6	Weierstrass function	1	[−100, 100]	10
CEC-7	Modified Schwefel function	1	[−100, 100]	10
CEC-8	Expanded Schaffer function	1	[−100, 100]	10
CEC-9	Happy CAT function	1	[−100, 100]	10

References

1. Chong, H.Y.; Yap, H.J.; Tan, S.C.; Yap, K.S.; Wong, S.Y. Advances of metaheuristic algorithms in training neural networks for industrial applications. Soft Comput.; 2021; 25, pp. 11209-11233. [DOI: https://dx.doi.org/10.1007/s00500-021-05886-z]

2. Osaba, E.; Villar-Rodriguez, E.; Del Ser, J.; Nebro, A.J.; Molina, D.; LaTorre, A.; Suganthan, P.N.; Coello, C.A.C.; Herrera, F. A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems. Swarm Evol. Comput.; 2021; 64, 100888. [DOI: https://dx.doi.org/10.1016/j.swevo.2021.100888]

3. Khan, M.K.; Zafar, M.H.; Mansoor, M.; Mirza, A.F.; Khan, U.A.; Khan, N.M. Green energy extraction for sustainable development: A novel MPPT technique for hybrid PV-TEG system. Sustain. Energy Technol. Assess.; 2022; 53, 102388.

4. Holland, J.H. Genetic algorithms. Sci. Am.; 1992; 267, pp. 66-73. [DOI: https://dx.doi.org/10.1038/scientificamerican0792-66]

5. Knowles, J.; Corne, D. The Pareto archived evolution strategy: A new baseline algorithm for Pareto multiobjective optimization. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406); Washington, DC, USA, 6–9 July 1999.

6. Mansoor, M.; Mirza, A.F.; Ling, Q. Harris hawk optimization-based MPPT control for PV Systems under Partial Shading Conditions. J. Clean. Prod.; 2020; 274, 122857. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.122857]

7. Mirjalili, S.; Gandomi, A.H.; Mirjalili, S.Z.; Saremi, S.; Faris, H.; Mirjalili, S.M. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv. Eng. Softw.; 2017; 114, pp. 163-191. [DOI: https://dx.doi.org/10.1016/j.advengsoft.2017.07.002]

8. Dorigo, M.; Birattari, M.; Stutzle, T. Ant colony optimization. IEEE Comput. Intell. Mag.; 2006; 1, pp. 28-39. [DOI: https://dx.doi.org/10.1109/MCI.2006.329691]

9. Mirjalili, S.; Lewis, A. The Whale Optimization Algorithm. Adv. Eng. Softw.; 2016; 95, pp. 51-67. [DOI: https://dx.doi.org/10.1016/j.advengsoft.2016.01.008]

10. Wu, Q.; Ma, Z.; Xu, G.; Li, S.; Chen, D. A novel neural network classifier using beetle antennae search algorithm for pattern classification. IEEE Access; 2019; 7, pp. 64686-64696. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2917526]

11. Amari, S.; Wu, S. Improving support vector machine classifiers by modifying kernel functions. Neural Netw.; 1999; 12, pp. 783-789. [DOI: https://dx.doi.org/10.1016/S0893-6080(99)00032-5]

12. Peterson, L.E. K-nearest neighbor. Scholarpedia; 2009; 4, 1883. [DOI: https://dx.doi.org/10.4249/scholarpedia.1883]

13. Rish, I. An empirical study of the I Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence; Seattle, WA, USA, 8 August 2001.

14. El Naqa, I.; Murphy, M. What Is Machine Learning?. Machine Learning in Radiation Oncology: Theory and Applications; El Naqa, I.; Li, R.; Murphy, M. Springer International Publishing: Cham, Switzerland, 2015; pp. 3-11.

15. Shanmuganathan, S. Artificial Neural Network Modelling: An Introduction. Artificial Neural Network Modelling; Shanmuganathan, S.; Samarasinghe, S. Springer International Publishing: Cham, Switzerland, 2016; pp. 1-14.

16. Sinha, A.K.; Hati, A.S.; Benbouzid, M.; Chakrabarti, P. ANN-based pattern recognition for induction motor broken rotor bar monitoring under supply frequency regulation. Machines; 2021; 9, 87. [DOI: https://dx.doi.org/10.3390/machines9050087]

17. D’Addona, D.M.; Ullah, A.M.M.S.; Matarazzo, D. Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. J. Intell. Manuf.; 2017; 28, pp. 1285-1301. [DOI: https://dx.doi.org/10.1007/s10845-015-1155-0]

18. Wu, J.; Xu, C.; Han, X.; Zhou, D.; Zhang, M.; Li, H.; Tan, K.C. Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell.; 2021; 44, pp. 7824-7840. [DOI: https://dx.doi.org/10.1109/TPAMI.2021.3114196] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34546918]

19. Kiranyaz, S.; Avci, O.; Abdeljaber, O.; Ince, T.; Gabbouj, M.; Inman, D.J. 1D convolutional neural networks and applications: A survey. Mech. Syst. Signal Process.; 2021; 151, 107398. [DOI: https://dx.doi.org/10.1016/j.ymssp.2020.107398]

20. Poznyak, A.; Chairez, I.; Poznyak, T. A survey on artificial neural networks application for identification and control in environmental engineering: Biological and chemical systems with uncertain models. Annu. Rev. Control.; 2019; 48, pp. 250-272. [DOI: https://dx.doi.org/10.1016/j.arcontrol.2019.07.003]

21. Hussain, M.; Dhimish, M.; Titarenko, S.; Mather, P. Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters. Renew. Energy; 2020; 155, pp. 1272-1292. [DOI: https://dx.doi.org/10.1016/j.renene.2020.04.023]

22. Veerasamy, V.; Wahab, N.I.A.; Othman, M.L.; Padmanaban, S.; Sekar, K.; Ramachandran, R.; Hizam, H.; Vinayagam, A.; Islam, M.Z. LSTM Recurrent Neural Network Classifier for High Impedance Fault Detection in Solar PV Integrated Power System. IEEE Access; 2021; 9, pp. 32672-32687. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3060800]

23. Jiang, J.; Li, W.; Wen, Z.; Bie, Y.; Schwarz, H.; Zhang, C. Series Arc Fault Detection Based on Random Forest and Deep Neural Network. IEEE Sens. J.; 2021; 21, pp. 17171-17179. [DOI: https://dx.doi.org/10.1109/JSEN.2021.3082294]

24. Han, F.; Jiang, J.; Ling, Q.H.; Su, B.Y. A survey on metaheuristic optimization for random single-hidden layer feedforward neural network. Neurocomputing; 2019; 335, pp. 261-273. [DOI: https://dx.doi.org/10.1016/j.neucom.2018.07.080]

25. Spurlock, K.; Elgazzar, H. A genetic mixed-integer optimization of neural network hyper-parameters. J. Supercomput.; 2022; 78, pp. 14680-14702. [DOI: https://dx.doi.org/10.1007/s11227-022-04475-7]

26. Abd Elaziz, M.; Dahou, A.; Abualigah, L.; Yu, L.; Alshinwan, M.; Khasawneh, A.M.; Lu, S. Advanced metaheuristic optimization techniques in applications of deep neural networks: A review. Neural Comput. Appl.; 2021; 33, pp. 14079-14099. [DOI: https://dx.doi.org/10.1007/s00521-021-05960-5]

27. Darwish, A.; Hassanien, A.E.; Das, S. A survey of swarm and evolutionary computing approaches for deep learning. Artif. Intell. Rev.; 2020; 53, pp. 1767-1812. [DOI: https://dx.doi.org/10.1007/s10462-019-09719-2]

28. Ho, Y.-C.; Pepyne, D.L. Simple explanation of the no-free-lunch theorem and its implications. J. Optim. Theory Appl.; 2002; 115, pp. 549-570. [DOI: https://dx.doi.org/10.1023/A:1021251113462]

29. Abualigah, L.; Abd Elaziz, M.; Sumari, P.; Geem, Z.W.; Gandomi, A.H. Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst. Appl.; 2022; 191, 116158. [DOI: https://dx.doi.org/10.1016/j.eswa.2021.116158]

30. Bou-Rabee, M.; Lodi, K.A.; Ali, M.; Ansari, M.F.; Tariq, M.; Sulaiman, S.A. One-month-ahead wind speed forecasting using hybrid AI model for coastal locations. IEEE Access; 2020; 8, pp. 198482-198493. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3028259]

31. Farayola, A.M.; Sun, Y.; Ali, A. ANN-PSO Optimization of PV systems under different weather conditions. Proceedings of the 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA); Paris, France, 14–17 October 2018; IEEE: Piscataway, NJ, USA, 2018.

32. Abdolrasol, M.G.; Mohamed, R.; Hannan, M.A.; Al-Shetwi, A.Q.; Mansor, M.; Blaabjerg, F. Artificial neural network based particle swarm optimization for microgrid optimal energy scheduling. IEEE Trans. Power Electron.; 2021; 36, pp. 12151-12157. [DOI: https://dx.doi.org/10.1109/TPEL.2021.3074964]

33. Banadkooki, F.B.; Ehteram, M.; Ahmed, A.N.; Teo, F.Y.; Ebrahimi, M.; Fai, C.M.; Huang, Y.F.; El-Shafie, A. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm. Environ. Sci. Pollut. Res.; 2020; 27, pp. 38094-38116. [DOI: https://dx.doi.org/10.1007/s11356-020-09876-w]

34. Wongsinlatam, W.; Buchitchon, S. Criminal cases forecasting model using a new intelligent hybrid artificial neural network with cuckoo search algorithm. IAENG Int. J. Comput. Sci.; 2020; 47, pp. 481-490.

35. Mehrabi, P.; Honarbari, S.; Rafiei, S.; Jahandari, S.; Alizadeh Bidgoli, M. Seismic response prediction of FRC rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques. J. Ambient. Intell. Humaniz. Comput.; 2021; 12, pp. 10105-10123. [DOI: https://dx.doi.org/10.1007/s12652-020-02776-4]

36. Anand, A.; Suganthi, L. Hybrid GA-PSO optimization of artificial neural network for forecasting electricity demand. Energies; 2018; 11, 728. [DOI: https://dx.doi.org/10.3390/en11040728]

37. Faris, H.; Aljarah, I.; Mirjalili, S. Improved monarch butterfly optimization for unconstrained global search and neural network training. Appl. Intell.; 2018; 48, pp. 445-464. [DOI: https://dx.doi.org/10.1007/s10489-017-0967-3]

38. Zafar, M.H.; Khan, N.M.; Mansoor, M.; Mirza, A.F.; Moosavi, S.K.R.; Sanfilippo, F. Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems. Energy Convers. Manag.; 2022; 258, 115564. [DOI: https://dx.doi.org/10.1016/j.enconman.2022.115564]

39. Zhang, J.; Wang, J.S. Improved salp swarm algorithm based on levy flight and sine cosine operator. IEEE Access; 2020; 8, pp. 99740-99771. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.2997783]

40. Iacca, G.; Junior, V.C.D.S.; de Melo, V.V. An improved Jaya optimization algorithm with Lévy flight. Expert Syst. Appl.; 2021; 165, 113902. [DOI: https://dx.doi.org/10.1016/j.eswa.2020.113902]

41. Dahou, A.; Abd Elaziz, M.; Chelloug, S.A.; Awadallah, M.A.; Al-Betar, M.A.; Al-qaness, M.A.; Forestiero, A. Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm. Comput. Intell. Neurosci.; 2022; 2022, 6473507. [DOI: https://dx.doi.org/10.1155/2022/6473507]

42. Xiong, J.; Peng, T.; Tao, Z.; Zhang, C.; Song, S.; Nazir, M.S. A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction. Energy; 2023; 266, 126419. [DOI: https://dx.doi.org/10.1016/j.energy.2022.126419]

43. Mirjalili, S. SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl. Based Syst.; 2016; 96, pp. 120-133. [DOI: https://dx.doi.org/10.1016/j.knosys.2015.12.022]

44. Chechkin, A.; Metzler, R.; Klafter, J.; Gonchar, V.Y. Introduction to the Theory Lévy Flights. Anomalous Transport: Foundations and Applications; Wiley-VCH: Weinheim, Germany, 2008.

45. Yang, X.-S.; Deb, S. Multiobjective cuckoo search for design optimization. Comput. Oper. Res.; 2013; 40, pp. 1616-1624. [DOI: https://dx.doi.org/10.1016/j.cor.2011.09.026]

46. Xin, Y.; Yong, L.; Guangming, L. Evolutionary programming made faster. IEEE Trans. Evol. Comput.; 1999; 3, pp. 82-102. [DOI: https://dx.doi.org/10.1109/4235.771163]

47. Sulaiman, M.H.; Mustaffa, Z.; Saari, M.M.; Daniyal, H. Barnacles mating optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell.; 2020; 87, 103330. [DOI: https://dx.doi.org/10.1016/j.engappai.2019.103330]

48. Kennedy, J.; Eberhart, R. Particle swarm optimization. Proceedings of the ICNN’95—International Conference on Neural Networks; Perth, Australia, 27 November–1 December 1995.

49. Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw.; 2014; 69, pp. 46-61. [DOI: https://dx.doi.org/10.1016/j.advengsoft.2013.12.007]

50. Abualigah, L.; Diabat, A.; Mirjalili, S.; Abd Elaziz, M.; Gandomi, A.H. The Arithmetic Optimization Algorithm. Comput. Methods Appl. Mech. Eng.; 2021; 376, 113609. [DOI: https://dx.doi.org/10.1016/j.cma.2020.113609]

51. Fouad, M.M.; El-Desouky, A.I.; Al-Hajj, R.; El-Kenawy, E.S.M. Dynamic Group-Based Cooperative Optimization Algorithm. IEEE Access; 2020; 8, pp. 148378-148403. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3015892]

52. Zafar, M.H.; Khan, N.M.; Moosavi, S.K.R.; Mansoor, M.; Mirza, A.F.; Akhtar, N. Artificial Neural Network (ANN) trained by a Novel Arithmetic Optimization Algorithm (AOA) for Short Term Forecasting of Wind Power. Proceedings of the International Conference on Intelligent Technologies and Applications (INTAP); Grimstad, Norway, 11–13 October 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 197-209.

53. Liang, J.J.; Suganthan, P.N.; Chen, Q. Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Comput. Intell. Lab. Zhengzhou Univ. Zhengzhou China Nanyang Technol. Univ. Singap. Tech. Rep.; 2013; 201212, pp. 281-295.

54. Mansoor, M.; Ling, Q.; Zafar, M.H. Short Term Wind Power Prediction using Feedforward Neural Network (FNN) trained by a Novel Sine-Cosine fused Chimp Optimization Algorithm (SChoA). Proceedings of the 2022 5th International Conference on Energy Conservation and Efficiency (ICECE); Lahore, Pakistan, 16–17 March 2022; IEEE: Piscataway, NJ, USA, 2022.

55. Available online: https://archive.ics.uci.edu/ml/index.php (accessed on 17 September 2022).

56. Available online: https://www.kaggle.com/datasets (accessed on 30 September 2022).

57. Al-Dahidi, S.; Ayadi, O.; Alrbai, M.; Adeeb, J. Ensemble approach of optimized artificial neural networks for solar photovoltaic power prediction. IEEE Access; 2019; 7, pp. 81741-81758. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2923905]

58. Şahin, S.; Türkeş, M. Assessing wind energy potential of Turkey via vectoral map of prevailing wind and mean wind of Turkey. Theor. Appl. Climatol.; 2020; 141, pp. 1351-1366. [DOI: https://dx.doi.org/10.1007/s00704-020-03276-3]

59. Available online: https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset (accessed on 13 October 2022).

60. Available online: https://www.kaggle.com/datasets/anikannal/solar-power-generation-data (accessed on 10 October 2022).

61. Zafar, M.H.; Khan, U.A.; Khan, N.M. Hybrid Grey Wolf Optimizer Sine Cosine Algorithm based Maximum Power Point Tracking Control of PV Systems under Uniform Irradiance and Partial Shading Condition. Proceedings of the 2021 4th International Conference on Energy Conservation and Efficiency (ICECE); Lahore, Pakistan, 16–17 March 2021; IEEE: Piscataway, NJ, USA, 2021.

62. Zafar, M.H.; Khan, N.M.; Mirza, A.F.; Mansoor, M.; Akhtar, N.; Qadir, M.U.; Khan, N.A.; Moosavi, S.K.R. A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustain. Energy Technol. Assess.; 2021; 47, 101367.

63. Mansoor, M.; Mirza, A.F.; Duan, S.; Zhu, J.; Yin, B.; Ling, Q. Maximum energy harvesting of centralized thermoelectric power generation systems with non-uniform temperature distribution based on novel equilibrium optimizer. Energy Convers. Manag.; 2021; 246, 114694. [DOI: https://dx.doi.org/10.1016/j.enconman.2021.114694]