Abstract
Based on the credibility theory, this paper is devoted to the fuzzy multiobjective programming problem. Firstly, the expected-value model of fuzzy multiobjective programming problem is provided based on credibility theory; then two new approaches for obtaining efficient solutions are proposed on the basis of the expected-value model, whose validity has been proven. For solving the fuzzy MOP problem efficiently, Latin hypercube sampling, fuzzy simulation, support vector machine, and artificial bee colony algorithm are integrated to build a hybrid intelligent algorithm. An application case study on availability allocation optimization problem in repairable parallel-series system design is documented. The results suggest that the proposed method has excellent consistency and efficiency in solving fuzzy multiobjective programming problem and is particularly useful for expensive systems.
Highlights
Many real-life problems have mainly been studied from the multiobjective optimization point of view; these problems require considering and optimizing multiple and conflicting objectives at the same time
A hybrid intelligent algorithm composed of Latin hypercube sampling (LHS), fuzzy simulation, support vector machine (SVM), and artificial bee colony (ABC) algorithm has been proposed to obtain the Pareto efficient solutions in fuzzy MOP problem (FMOP) problem based on credibility theory in this paper
Two new solution approaches were proposed to generate Pareto efficient solutions for FMOP problem based on credibility theory, which are different from the traditional FMOP solution methods
Summary
Many real-life problems have mainly been studied from the multiobjective optimization point of view; these problems require considering and optimizing multiple and conflicting objectives at the same time. K. Liu [10] proposed a self-dual set function, credibility measure, which can be considered as the improvement of possibility measure in the fuzzy decision system, and an axiomatic foundation based on credibility measure, called credibility theory, has been developed [11, 12]. The linear weighted method and the ideal point method are proposed to obtain Pareto efficient solutions in FMOP problem based on credibility theory. A new powerful and efficient hybrid intelligent algorithm should be designed and applied to the FMOP problem to reduce the computation cost and improve the computation accuracy For this purpose, a hybrid intelligent algorithm composed of Latin hypercube sampling (LHS), fuzzy simulation, support vector machine (SVM), and artificial bee colony (ABC) algorithm has been proposed to obtain the Pareto efficient solutions in FMOP problem based on credibility theory in this paper.
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