Metaheuristic approaches for solving multiobjective optimization problems

Abstract

Multiobjective optimization problems (MOOPs) require optimizing two or more, often conflicting objectives. The wide application of MOOPs has attracted the attention of researchers in academics and industry; therefore, a great deal of effort has been made to develop effective approaches toward solving MOOPs. In this chapter, we introduce a new metaheuristic approach called multiobjective electric fish optimization (MOEFO). The proposed approach is based on the Electric Fish Optimization (EFO) algorithm, a recently proposed metaheuristic algorithm for single-objective problems. Since EFO has achieved significant performance on solving different types of problems such as constrained and unconstrained problems, it is extended here for solving MOOPs efficiently. The proposed approach is compared with well-known meta-heuristics in the literature, and the experimental results show that MOEFO is among the best algorithms for solving MOOPs within a competitive running time. Moreover, it becomes very competitive for solving challenging Many-objective optimization problems (MaOPs) having four or more objectives.