Ameliorated elk herd optimizer for global optimization and engineering problems

Abstract

Optimization techniques have received significant attention for reliably addressing practical problems. A potential meta-heuristic called elk herd optimizer (EHO) was created, inspired by the social behavior and reproduction of elks. EHO has drawbacks, including poor convergence competency and a tendency to fall into local extrema in various optimization problems. Furthermore, this algorithm does not account for the memory of its search agents and has difficulty effectively balancing exploration and exploitation, which can lead to early convergence toward a local optimum. This study addresses the above issues by proposing an ameliorated EHO (AEHO) by incorporating several modifications into the basic EHO algorithm, which can be described as follows: A new hybrid memory-based EHO is developed that uses the particle swarm optimization (PSO) algorithm to guide EHO to search for reasonable candidate solutions. This hybrid approach was proposed to enhance EHO’s diversity and balance search capabilities to achieve strong search performance. Initially, a memory component was added to EHO using the idea of pbest from PSO to tap into promising search regions, which focuses on improving the best solutions and preventing the algorithm from getting stuck in a local optimum. In addition, the PSO concepts of (gbest) and (pbest) are used to enhance the best placements of the search agents in EHO. Finally, a greedy selection method was used to improve the efficiency of exhaustive exploration in AEHO, using the fitness values before and after updates as an indicator for efficacy of the best solutions. To evaluate the performance of the AEHO algorithm against a group of well-known competitors, we use ten complex test functions from the global CEC2022 test suite and thirty complex test functions from the global CEC2014 test suite. Based on the analysis of the experimental findings, AEHO performed optimally on 84% of the CEC2014 functions and 74% of the CEC2022 functions, ranking first in both suites with an average ranking of 3.11 and 1.62, respectively. The mean computation time of AEHO is about one-third of the average computation time for the first-ranked method, indicating that AEHO not only performs very well in global searches but also exhibits greater search efficiency when compared to newer optimization algorithms. The applicability and reliability of AEHO were thoroughly studied on four constrained engineering design problems and a real-world industrial process. The results demonstrate the superiority and promising potential of AEHO in addressing a wide range of challenging real-world problems.