Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO

Abstract

This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions.

Abba Suganda Girsang 1 and Chun-Wei Tsai 2 and Chu-Sing Yang 3

Academic Editor:Katsuhiro Honda

1, Master of Information Technology at Binus Graduate Program, Bina Nusantara University, Jalan Kebon Jeruk Raya No. 27, Jakarta 11530, Indonesia
2, Department of Computer Science and Information Engineering, National Ilan University, Yilan 26041, Taiwan
3, Institute of Computer and Communication Engineering and Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan

Received 26 August 2015; Accepted 8 October 2015; 27 October 2015

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

One important issue in comparison matrix of AHP is the consistency. In multicriteria decision making (MCDM), decision makers (DMs) reveal their opinion to choose some decision alternatives by a comparison matrix [1]. However, the comparison matrix which is identified as inconsistent cannot be used as a judgment. Meanwhile, the consistency is hard to obtain, when evaluating a large number of criteria.

There are two models of a comparison matrix, multiplicative preference relations [1] and fuzzy preference relations [2, 3]. The element comparison matrix of multiplicative preference relation is stated as [figure omitted; refer to PDF] which defines the dominance of alternative [figure omitted; refer to PDF] over [figure omitted; refer to PDF] , where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] . On fuzzy preference relations, element comparison matrix is stated as [figure omitted; refer to PDF] , which defines the preference of alternative [figure omitted; refer to PDF] over [figure omitted; refer to PDF] , where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] . This study focuses on fuzzy preference relations.

The issues of consistency in fuzzy preference relation also have received attention from researchers. Xu and Wang [4] proposed a revised approach by using linear programming models to generate the priority weights for additive interval fuzzy preference relations. Xu and Chen [5] presented the method to fulfill the element, which is incomplete on fuzzy preference for group decision making based on additive transitive consistency and accumulates the auxiliary value into a group auxiliary relation. This research was extended by Xu et al. [6], who deduced a function between the additive transitivity fuzzy preference and its corresponding priority vector. Xu et al. [7] proposed algorithm by eliminating the cycles of length 3 to [figure omitted; refer to PDF] in the digraph of the incomplete reciprocal preference relation and converted it to the one with ordinal consistency. Liu et al. [8] proposed a method to solve the incompleteness of fuzzy preference matrix and also repair the inconsistency preference matrix. This method calculated minimal of the squared error of the incomplete fuzzy preference relation and its priority weight vector to fulfill the missing values and generated the consistency fuzzy preference such that the modified one is the closest to the original one. Chen et al. [9] presented a method for group decision making using incomplete fuzzy preference based on additive consistency. Chiclana et al. [10] proposed a functional equation to model the cardinal consistency in the strength of preferences of reciprocal preference relations. Xia et al. [11] improved the consistency by using the geometric consistency index in complete and incomplete fuzzy preference.

A research using swarm intelligence was also used to solve the inconsistent comparison matrix such as PSO which combines Taguchi method [12]. It improved the previous research using genetic algorithm [13] to solve the inconsistent comparison matrix. Both researches used the same objective function to solve the problem, that is, summing the CR and deviation matrix. Although successful metaheuristic to solve that problem, the variations of implemented metaheuristic, is rarely conducted. Girsang et al. [14, 15] also already implemented the ant colony optimization (ACO) approach in our previous research to solve this problem with the different objective function that uses Yang et al. [12] and Lin et al. [13]. In [14], besides repairing the inconsistent ratio, ACO is used to enhance the minimal deviation matrix, while in [15] ACO is used to enhance the minimal consistent ratio. It becomes a promising research to consider both of the two objective functions using swarm intelligence. Girsang et al. [16] also implemented PSO with multiobjective approach; however, it only focuses on repairing the multiplicative preference matrix.

2. Related Work

2.1. Consistent Ratio in AHP

A simple illustration about inconsistency is described as follows. The decision maker (DM) has opinion that [figure omitted; refer to PDF] is bigger than [figure omitted; refer to PDF] and [figure omitted; refer to PDF] is bigger than [figure omitted; refer to PDF] . The consistent logic of this case is that [figure omitted; refer to PDF] should be bigger than [figure omitted; refer to PDF] . Contrarily, it would be inconsistent if DM said that [figure omitted; refer to PDF] is bigger than [figure omitted; refer to PDF] . In AHP, the opinion of decision makers is represented in a comparison matrix. An element comparison matrix can reflect the subjective opinion that expose strength of the preference and the feeling. In a fuzzy preference matrix, the element of comparison matrix, [figure omitted; refer to PDF] , can be expressed as [figure omitted; refer to PDF] , with a scale value ( [figure omitted; refer to PDF] ) where [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] . Matrix [figure omitted; refer to PDF] as Fuzzy preference relation can be depicted as follows: [figure omitted; refer to PDF]

To measure the multiplicative consistency in a comparison matrix, Saaty defined consistent ratio (CR). He proposed that the threshold of CR in multiplicative preference matrixes is 0.1. The CR is defined as [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the eigenvalue and eigenvector of the matrix, respectively. Further, CI is the consistency index; [figure omitted; refer to PDF] represents number criteria or size matrix, and the RI (random consistency index) is the average index of randomly generated weights. The value of RI on each size matrices is described in Table 1. A CR less than 0.1 can be categorized as consistent matrix. Perfect consistency is obtained when the maximum eigenvalue equal to the number criteria ( [figure omitted; refer to PDF] ).

Table 1: Random consistency index (RI).

Number criteria	1	2	3	4	5	6	7	8	9
	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45

Herrera-Viedma et al. [17] proposed some definitions to reveal the consistency in a fuzzy preference matrix. They show that the additive consistency is more appropriate to define the degree of consistency of fuzzy preference matrix. The relation in matrix [figure omitted; refer to PDF] is consistent if the element matrix can satisfy (5) and (6): [figure omitted; refer to PDF] where [figure omitted; refer to PDF]

Xu and Da [18] proposed determining the multiplicative consistency in the fuzzy preference matrix. They used Xu's [19] approach to determine CI in multiplicative preference matrix. Suppose [figure omitted; refer to PDF] is the element of multiplicative of preference matrix. Xu [19] defined the CI in (7). This equation is derived from (3). By knowing the weight of each criterion and element of matrix, CI can be obtained: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is element matrix of multiplicative preference matrix and [figure omitted; refer to PDF] is the weight of each criterion. To determine the CI in a fuzzy preference matrix, [4, 18] used converting with assumption [figure omitted; refer to PDF] , where [figure omitted; refer to PDF] is element matrix of the fuzzy preference matrix. Therefore, they proposed determining CI as in [figure omitted; refer to PDF]

2.2. Deviation Matrix

While the consistent ratio is repaired, the modified matrix automatically generates the deviation matrix from the original. Ideally, the modified matrices are kept closer to their original matrices in order to maintain the original judgment. It means that the deviation matrix is enriched to be minimal. There are some methods to represent the deviation, such as difference index ( [figure omitted; refer to PDF] ) [13] and [figure omitted; refer to PDF] and [figure omitted; refer to PDF] . Difference index (Di) is defined as the real difference between the same gene values in two genotypes. The other deviation is defined as [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , which is denoted as [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the original matrix [ [figure omitted; refer to PDF] ]; [figure omitted; refer to PDF] is the modified matrix [ [figure omitted; refer to PDF] ], and [figure omitted; refer to PDF] is the matrix size.

In the multiplicative preference matrix, the difference index (Di) is generally used to measure the distance between two matrices. However, in fuzzy preference matrix, [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are considered as more appropriate to represent the deviation matrix. Since the value preference will be 0.51 to 1 or 0 to 0.49, the division each all genes in Di will not be different significantly. As a consequence, the difference of two matrices will not be significant as well. Therefore, in this study, instead of using Di, [figure omitted; refer to PDF] is employed to define the deviation matrix for the preference fuzzy matrix.

2.3. Particle Swarm Optimization

PSO was firstly proposed by Kennedy and Eberhart [20]. It is population-based stochastic optimization on the social behaviors observed in animals or insects such as bird flocking, fish schooling, and animal herding. In PSO, each particle of swarm represents the solution which moves to search the optimal solutions. Each particle also broadcasts its current position to neighbour particles. The position of each particle is adjusted according to its velocity and the best position it has found so far. A particle [figure omitted; refer to PDF] starts moving with a velocity [figure omitted; refer to PDF] from its current position [figure omitted; refer to PDF] , to the next position [figure omitted; refer to PDF] , as in (11). The velocity is influenced by three factors: (a) previous velocity [figure omitted; refer to PDF] , (b) the best previous particle position [figure omitted; refer to PDF] , and (c) the best previous swarm particle position [figure omitted; refer to PDF] . It can be stated as (12): [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the weight to control the convergence of the velocity, [figure omitted; refer to PDF] the acceleration weight cognitive element, [figure omitted; refer to PDF] the weight of social parameter, and [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are random numbers in the range [ [figure omitted; refer to PDF] ].

3. Proposed Method

3.1. PSOMOF Algorithm

In PSO, each particle seeks the best position by moving in the search space. The position in PSO can represent an element in the comparison matrix. As shown in previous section, encoding position of element matrix can be encoded only from lower triangular matrix. If matrix [figure omitted; refer to PDF] is identified as an inconsistent matrix and needs to be repaired, then the scale value of matrix should be changed with new value. To be efficient, the whole elements of comparison matrix can be represented by lower triangular matrix. Therefore, the position of PSO that should be changed can be represented only by the lower triangular matrix. When changing the value of each node to be consistent, it also changes the rate consistent ratio (CR) and deviation matrix. We use [figure omitted; refer to PDF] to represent the deviation matrix in this method. Changing the value of each node means changing the particle's position. In PSO, the position is affected by a particles historical best position (local best) and the swarms' best position (global best). The solution (new value position) is performed to chase the consistent rate. However, as previously mentioned, there is no one solution which can achieve CR and [figure omitted; refer to PDF] minimal at the same time. PSOMOF algorithm is proposed by constructing the nondominated solutions which depicts the relation between [figure omitted; refer to PDF] and CR. Algorithm 1 shows the outline of PSOMOF algorithm. In this method, there are three steps in which each step uses PSO to get the result matrices:

(1) Minimize [figure omitted; refer to PDF] Step . Firstly, each particle (there are 200 particles) generates its position and its velocity randomly. The position particle means that the particle generates randomly the candidate for the modified matrix. The element matrix can be represented only by the lower triangular matrix elements consecutively. The velocity particle means that the particle generates the value as adding/diminishing the position of the particles. The initial position of each particle [figure omitted; refer to PDF] is set the same as the original position. The initial velocity of each particle [figure omitted; refer to PDF] is set randomly but lower than 0.1. The best historical particle is defined as [figure omitted; refer to PDF] , and the best position for all particles is defined as [figure omitted; refer to PDF] . Initially, [figure omitted; refer to PDF] is taken from the first position particle generated, while [figure omitted; refer to PDF] is taken from the best position from the first position of all particles generated. In the next iteration, based on the previous velocity information, [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and some variables ( [figure omitted; refer to PDF] ), the velocity of each particle is updated as described in (12). To set the value of variables, some experiments are conducted and the new position will be obtained based on the updated velocity, as described in (11). The evaluation of the fitness function is minimizing. However, if a particles [figure omitted; refer to PDF] is worse than before, or [figure omitted; refer to PDF] , the update will be cancelled. The result of this fitness function also updates the new best historical position of each particle ( [figure omitted; refer to PDF] ) and the new best position of all particles as a group ( [figure omitted; refer to PDF] ). This process is repeated until the iteration maximum is reached.

(2) Minimize CR Step. It is almost the same as step ( [figure omitted; refer to PDF] ). If step ( [figure omitted; refer to PDF] ) minimizes [figure omitted; refer to PDF] as its fitness function, then step ( [figure omitted; refer to PDF] ) minimizes CR as its fitness function.

(3) Obtain a Set of Nondominated CR- [figure omitted; refer to PDF] Solutions Step. It is also the same as the process to minimize [figure omitted; refer to PDF] . Yet the process adds some various CRs, which are decreased gradually until CRmin is reached.

Algorithm 1: PSOMOF algorithm.

Initialize ()

Minimize σ ()

For each particle generates the position and velocity randomly.

Xp [arrow left] the initial position // Xp is the particles best historical

Xg [arrow left] the initial position // Xg is the best of all particles

Repeat

Determine velocity using (12).

Update new position particle using (11).

Determine [figure omitted; refer to PDF] of new position using (10). If the new position has a lower [figure omitted; refer to PDF] and CR < 0.1 updated new position is allowed

otherwise, update new position is canceled and keeping the current position.

Choosing the new Xp and Xg based on value σ

Until max iterations is reached

Get minimal σ

Minimize CR ()

For each particle generates the position and velocity randomly.

Xp [arrow left] the initial position // Xp is the particles best historical

Xg [arrow left] the initial position // Xg is the best of all particles

Repeat

Determine velocity using (12).

Update new position particle using (11).

Determine CR of new position using (4). If the new position has a lower CR and CR < 0.1 updated new position is allowed

otherwise, update new position is canceled and keeping the current position.

Choosing the new Xp and Xg based on value CR

Until max iterations is reached

Get minimal CR

Minimize CR-σ ()

CRo [arrow left] 0.1

Xp [arrow left] the initial position // Xp is the particles best historical

Xg [arrow left] the initial position // Xg is the best of all particles

While CRmin < CRo

Repeat

Determine velocity using (12).

Update new position particle using (11).

Determine CR of new position using (4). If the new position has a lower CR and CR < 0.1 updated new position is allowed

otherwise, update new position is canceled and keeping the current position.

Choosing the new Xp and Xg based on value CR

Until max iterations is reached

Store the modified matrix and its CR, [figure omitted; refer to PDF]

CRo [figure omitted; refer to PDF] CRo - k // k is small value, in this study [figure omitted; refer to PDF]

End While

Get matrices with their CR, [figure omitted; refer to PDF]

3.2. Encoding and Fractioning of Original Element Matrix

The encoding of matrix can be assembled by picking all elements in matrix. However, because the elements of fuzzy preference matrix (FPM) have a relation such that [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , encoding node can only encode the lower triangular elements of the matrix as nodes: [figure omitted; refer to PDF]

Equation (13) shows matrix [figure omitted; refer to PDF] with [figure omitted; refer to PDF] and its encoding for FPM by picking row by row sequentially in the elements of the lower triangular matrix. The number element of encoding [figure omitted; refer to PDF] can be determined [figure omitted; refer to PDF] . To obtain consistent matrix, of course, the value of each element matrix should be changed to a new value. The new values are chosen from the values of several candidates. Candidates elements are generated using the original fractioned value. If the original element is more than 0.5, the candidates will be between 0.5 and 1; if the original element is less than 0.5, the candidates will be between 0 and 0.5; if the original element is 0.5 (neutral), the candidate is still 0.5, or the original data should not be fractioned. This approach makes the candidate element not change the judgment tendency but will only change the judgment weight. The number of candidate elements is based on the fraction factor ( [figure omitted; refer to PDF] ). For example, if [figure omitted; refer to PDF] , then the number candidate will be [figure omitted; refer to PDF] . Suppose that matrix [figure omitted; refer to PDF] , [figure omitted; refer to PDF] is one of the original elements on node [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] is the matrix size; thus, the sequence of nodes traveled, [figure omitted; refer to PDF] , can be defined as [figure omitted; refer to PDF]

Each element origin, [figure omitted; refer to PDF] , is fractioned into several candidate elements [figure omitted; refer to PDF] , where [figure omitted; refer to PDF] denotes the index of the candidate element, as described in [figure omitted; refer to PDF] where [figure omitted; refer to PDF] ; [figure omitted; refer to PDF] .

Table 2 shows the original element and its candidate as a result of being fractioned if [figure omitted; refer to PDF] = 0.01. There are 50 candidates to substitute for the origin element.

Table 2: The original element and its candidate with [figure omitted; refer to PDF] .

Origin element	Candidate element
0.5	0.5
0, 0.1, 0.2, 0.3, 0.4	0, 0.01, 0.02, ..., 0.48, 0.49
0.6, 0.7, 0.8, 0.9, 1	0.51, 0.52, 0.53, ..., 0.99, 1

These fractioned elements can be used as candidate nodes to travel by particle in PSOMOF. The particle will move from the candidate in one node to the candidate in the next node. However, it is possible that the particle preserves the original element.

3.3. Determining CI in a Fuzzy Preference Matrix

Determining CI on fuzzy preference matrix by using (8) as a transforming from (7) is not suitable. Transformation using operation [figure omitted; refer to PDF] is not appropriate because it can exceed the threshold of the multiplicative matrix element. For example, suppose [figure omitted; refer to PDF] and [figure omitted; refer to PDF] . By transforming the above formula, [figure omitted; refer to PDF] will be 19. The value [figure omitted; refer to PDF] exceeds 9, which is the threshold of the multiplicative element matrix. Therefore, in this study, we use a method to transform the fuzzy preference ( [figure omitted; refer to PDF] ) to the multiplicative preference ( [figure omitted; refer to PDF] ) as introduced by Herrera-Viedma et al. [17] to determine the multiplicative consistency as shown in [figure omitted; refer to PDF]

Therefore, if the element fuzzy preference matrix [figure omitted; refer to PDF] , it can be transformed to be the element multiplicative matrix [figure omitted; refer to PDF] . This transformation value is not higher than the maximum scale of 9. By using (16), a new formula is proposed to determine the CI, as shown in [figure omitted; refer to PDF]

To prove this formula, the consistent ratio rate of one sample matrix as shown in (13) is determined. This sample matrix is selected from Xu et al. [6]. According to (5), obviously, matrix [figure omitted; refer to PDF] can be verified as a consistent matrix. Contrarily, matrix [figure omitted; refer to PDF] is identified as an inconsistent matrix with [figure omitted; refer to PDF] when it is determined by (4) and (7). However, if (4) and (18) are used, CR will be 0.05 and therefore will be a consistent matrix as in the result of (5). Therefore, in this research, (18) is used to define the CI value.

4. Experimental Results

4.1. Parameter Setting

Table 3 shows the parameter settings for the proposed and compared methods. The inconsistent matrix can be taken from the real life application which needs the decision maker opinions of comparing several criteria to get some alternatives. Once the matrix is identified as inconsistent, PSOMOF is able to be used to repair the inconsistent matrix. To see the performance of proposed methods in repairing inconsistent matrices, there are 15 inconsistent fuzzy preference matrices which need to be repaired as shown in Table 4. Some matrices come from the other papers, but some matrices are created randomly.

Table 3: Parameter settings for the PSOMOF, NSGA-2, and MOPSO.

Parameter	Value
PSOMOF:
[figure omitted; refer to PDF]	0.1
[figure omitted; refer to PDF]	0.2
[figure omitted; refer to PDF]	0.3
[figure omitted; refer to PDF]	0.4
[figure omitted; refer to PDF]	0.5
NSGA-2
Population size	100
Generation	200
Rate crossover	0.9
Rate mutation	0.1
MOPSO
Number of particle	20
Number of cycles	1000

Table 4: The dataset inconsistency matrices.

Matrix	Elements of lower triangular matrix	CR
Size [figure omitted; refer to PDF]
[figure omitted; refer to PDF]	0.9-0.4-0.2-0.3-0.6-0.1 [figure omitted; refer to PDF]	0.687
[figure omitted; refer to PDF]	0.8-0.4-0.1-0.1-0.3-0.7	0.364
[figure omitted; refer to PDF]	0.4-0.6-0.4-0.7-0.4-0.3 [figure omitted; refer to PDF]	0.183
[figure omitted; refer to PDF]	0.4-0.3-0.4-0.3-0.1-0.9	0.427
Size [figure omitted; refer to PDF]
[figure omitted; refer to PDF]	0.4-0.3-0.4-0.7-0.8-0.2-0.4-0.4-0.6-0.2	0.319
[figure omitted; refer to PDF]	0.1-0.2-0.3-0.9-0.6-0.8-0.7-0.4-0.6-0.3	0.343
[figure omitted; refer to PDF]	0.7-0.2-0.1-0.3-0.8-0.8-0.7-0.1-0.6-0.4	0.359
[figure omitted; refer to PDF]	0.1-0.3-0.1-0.8-0.8-0.4-0.6-0.8-0.6-0.7	0.479
Size [figure omitted; refer to PDF]
[figure omitted; refer to PDF]	0.8-0.2-0.1-0.4-0.8-0.9-0.4-0.2-0.4-0.7-0.9-0.8-0.7-0.4-0.3	0.440
[figure omitted; refer to PDF]	0.3-0.1-0.8-0.8-0.3-0.7-0.2-0.4-0.4-0.7-0.7-0.6-0.4-0.8-0.1	0.531
[figure omitted; refer to PDF]	0.2-0.8-0.1-0.7-0.8-0.4-0.4-0.6-0.7-0.6-0.1-0.4-0.6-0.3-0.7	0.437
Size [figure omitted; refer to PDF]
[figure omitted; refer to PDF]	0.7-0.2-0.4-0.7-0.3-0.6-0.4-0.3-0.9-0.2-0.7-0.4-0.6-0.8-0.8-0.8-0.3-0.9-0.2-0.7-0.9	0.315
[figure omitted; refer to PDF]	0.7-0.8-0.3-0.4-0.6-0.7-0.2-0.7-0.2-0.3-0.8-0.3-0.3-0.2-0.6-0.4-0.7-0.3-0.2-0.1-0.7	0.353
Size [figure omitted; refer to PDF]
[figure omitted; refer to PDF]	0.8-0.8-0.8-0.3-0.6-0.8-0.7-0.7-0.4-0.7-0.7-0.9-0.7-0.4-0.4-0.4-0.3-0.3-0.4-0.7-0.2-0.6-0.2-0.2-0.8-0.7-0.2.0.7	0.313
[figure omitted; refer to PDF]	0.7-0.8-0.7-0.3-0.8-0.6-0.4-0.7-0.2-0.2-0.7-0.3-0.8-0.7-0.3-0.1-0.3-0.1-0.2-0.8-0.8-0.3-0.8-0.1-0.6- 0.2-0.1-0.4	0.457

Data on a and b is picked from [8, 18].

4.2. Generating Nondominated Solutions

As aforementioned, there are two objectives for this proposed method, that is, the best CR and deviation matrix. Both objectives will conflict each other. When the CR is lowest (good consistent ratio), it leads to the highest (the worst) deviation, and vice versa. However, in order to get the acceptable matrix, the CR of modified matrix is limited below 0.1. It makes the solution consist of some relations ("CR-deviation") which can be identified as nondominated solutions. Equations (19a), (19b), and (19c) display the performance of PSOMOF to get the best CR- [figure omitted; refer to PDF] for [figure omitted; refer to PDF] , respectively. The origin matrix [figure omitted; refer to PDF] (19a) can be transformed to the modified matrices which have the best CR (19b) and [figure omitted; refer to PDF] (19c), respectively:

: CR = 0.319 [figure omitted; refer to PDF]

: CR = 0.003 and [figure omitted; refer to PDF] = 0.161 [figure omitted; refer to PDF]

: CR = 0.099 and [figure omitted; refer to PDF] = 0.073 [figure omitted; refer to PDF]

PSOMOF splits the method into three steps. These are to find the optimal deviation, optimal CR, and the optimal deviation with the particular value of CR. Figure 1 shows the process convergence to find the optimal deviation, while Figure 2 shows process convergence to find the optimal CR. Both of them are conducted on [figure omitted; refer to PDF] .

Figure 1: The process convergence to find the optimal deviation on [figure omitted; refer to PDF] .

[figure omitted; refer to PDF]

Figure 2: The process convergence to find the optimal CR on [figure omitted; refer to PDF] .

[figure omitted; refer to PDF]

After obtaining the minimal CR and [figure omitted; refer to PDF] , the third step of the PSOMOF is executed to get the nondominated CR-deviation nodes. By using PSOMOF, for each CR, the optimal deviation can be obtained. This proposed method thus successfully generates some nodes as solutions. Figure 3 shows the Pareto graph which depicts the relation of CR and deviation of matrix. The sample matrices for fuzzy preference matrix are [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] . It shows clearly that they will be contradictory to each other. In case of matrices, when [figure omitted; refer to PDF] is minimized, CR is maximized. Likewise, when CR is minimized, [figure omitted; refer to PDF] is maximized.

Figure 3: The Pareto graph solutions which show relation CR- [figure omitted; refer to PDF] .

[figure omitted; refer to PDF]

4.3. Comparison with Other Methods

To evaluate the performance of PSOMOF, this study uses the metric analysis [21, 22]. The performance is represented by the Pareto graph 10 times. The Pareto graph is then compared with Pareto graphs of two other algorithms, NSGA-2 [23] and MOPSO [24]. The Pareto-optimal set is generated by merging all of the Pareto graphs of all algorithms (PSOMOF, NSGA-2, and MOPSO) into a single Pareto solution. The nondominated solutions for each algorithm are generated by executing each algorithm once on a sample inconsistent matrix ( [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] ). There are 3 metrics to measure the performance of nondominated solutions achieved using the proposed method. Suppose a set of nondominated solutions [figure omitted; refer to PDF] .

[figure omitted; refer to PDF] Metric . This metric measures the average distance of the resulting nondominated set solutions to the Pareto-optimal set solutions. The better value should be a lower [figure omitted; refer to PDF] . It can be defined as desribed in [figure omitted; refer to PDF]

[figure omitted; refer to PDF] Metric . This metric measures the number of distribution nondominated solutions which are covered by a neighbourhood parameter [figure omitted; refer to PDF] . A bigger [figure omitted; refer to PDF] indicates better performance. [figure omitted; refer to PDF] can be defined as [figure omitted; refer to PDF]

[figure omitted; refer to PDF] Metric . This metric measures the extent of nondominated sets obtained. A wide range of values should be covered by the nondominated solutions. The bigger [figure omitted; refer to PDF] is better. [figure omitted; refer to PDF] can be defined as [figure omitted; refer to PDF]

The comparison results are shown in Table 5. It shows that PSOMOF [figure omitted; refer to PDF] metric is minimal in all of matrices compared to MOPSO and NSGA-2. These results show that most of the Pareto graphs of PSOMOF are closer to the Pareto-optimal front than both algorithms (NSGA-2 and MOPSO). For [figure omitted; refer to PDF] metric, the PSOMOF result is larger than both of the other algorithms except for [figure omitted; refer to PDF] . This indicates that the solutions of the proposed method are more distributed than both algorithms. In the [figure omitted; refer to PDF] metric, the proposed algorithm also outperforms as compared to the NSGA-2 and MOPSO. The proposed method returned nondominated solutions further than both of the other algorithms. Regarding this result, the proposed method, PSOMOF, can be claimed as the better algorithm compared to the two algorithms (NSGA-2 and MOPSO).

Table 5: Comparison of the metric performance of NSGA-2, MOPSO, and MOBAF.

Method	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
NSGA-2
[figure omitted; refer to PDF]	0.000896	0.000913	0.00127	0.00146	0.00133
[figure omitted; refer to PDF]	24.0	26.2	15.9	20.4	14.1
[figure omitted; refer to PDF]	1.76	1.50	1.34	1.21	1.11
MOPSO
[figure omitted; refer to PDF]	0.000830	0.000701	0.00110	0.000930	0.00122
[figure omitted; refer to PDF]	28.7	27.9	19.6	26.1	17.7
[figure omitted; refer to PDF]	1.89	1.64	1.60	1.39	1.29
PSOMOF
[figure omitted; refer to PDF]	0.000728	0.000688	0.000957	0.000926	0.000998
[figure omitted; refer to PDF]	32.5	27.9	20.6	25.7	19.0
[figure omitted; refer to PDF]	1.98	1.92	1.65	1.58	1.48

5. Conclusions

This paper presents a study to use the multiobjective PSO to solve the inconsistent fuzzy preference matrix in AHP, called PSOMOF. There are two objectives (consistent ratio and deviation matrix) considered in rectifying the matrix in order to be consistent. However, they are conflicting in that process. Therefore, the proposed algorithms offer some nondominated solutions which also satisfied the acceptable consistent matrices. The process in PSOMOF is split into three parts in which each part applies the PSO process. To see the performance, 15 inconsistent comparison matrices are repaired by the proposed methods. Besides repairing inconsistent comparison matrices, the proposed method also can generated some nondominated solution which can be classified as optimal solutions. This result shows the PSO algorithm is the potential approach to solve the inconsistent comparison matrix in AHP. The other intelligent algorithm also might be used to solve this problem. Further, this proposed method might be a potential method to combine with other method metaheuristic (hybrid) [figure omitted; refer to PDF] to improve the quality of results.

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the Ministry of Science and Technology of Taiwan, under Contracts MOST103-2221-E-197-034, MOST 104-2221-E-197-005, and NSC 100-2218-E-006-028-MY3.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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