A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms

The multi-objective optimization problem is difficult to solve with conventional methods and algorithms because there are conflicts among several objectives functions. Through the efforts of researchers experts from different fields for last 30 years, research application evolutionary (MOEA) have made excellent progress in solving such problems. MOEA has become one primary used technologies realm optimization. It also a hotspot computation community. This survey provides comprehensive investigation that emerged recent decades summarizes classifies classical MOEAs by mechanism viewpoint search strategy. paper divides them into three categories considering strategy MOEA, i.e., decomposition-based algorithms, dominant relation-based evaluation index-based algorithms. selects relevant representative detailed summary analysis. As prospective direction, we propose combine chaotic evolution algorithm these strategies improving capability new been discussed, which further proposes future direction MOEA. lays foundation development works future.