ABSTRACT

Optimization methods for solving multiobjective optimization problems (MOPs) can be classified into three categories: enumerative methods, deterministic methods, and stochastic methods. The following summarizes some major stochastic optimization methods widely used for MOPs, including evolutionary algorithms, swarm algorithms, and the tabu search (TS). Evolutionary algorithms emulate the process of natural selection for which the philosopher Herbert Spencer coined the phrase "survival of the fittest". There are in general three proposed classes: evolutionary programming, evolution strategies, and genetic algorithms (GAs). The chapter focuses on the algorithms addressing MOPs; details are given for the distinct features found in major multiobjective evolutionary algorithms (MOEAs). The multiobjective genetic algorithm (MOGA) was proposed by Fonseca and Fleming. It has three features: a modified ranking scheme, modified fitness assignment, and niche count. The Particle swarm optimization (PSO) has been successful in a wide variety of optimization tasks. However, it was once considered unsuitable to deal with multiobjective optimizations until the extension made by Coello Coello.