WIGA: Wolbachia Infection Genetic Algorithm for Solving Multi-Objective Optimization Problems

摘要

This paper introduces a new evolutionary algorithm for solving multi-objective optimization problems. The proposed algorithm simulates the infection of the endosymbiotic bacteria Wolbachia to improve the evolutionary search. We conducted a series of experiments to compare the results of the proposed algorithm to those obtained by state of the art multi-objective evolutionary algorithms (MOEAs) at solving the ZDT test suite. Our experimental results show that the proposed model outperforms established MOEAs at solving most of the test problems.