Using a simulated Wolbachia infection mechanism to improve multi-objective evolutionary algorithms
Indexed on: 28 Nov '13Published on: 28 Nov '13Published in: Natural computing
Abstract
This paper presents 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 computational experiments to contrast the results of the proposed algorithm to those obtained by state of the art multi-objective evolutionary algorithms (MOEAs). We employed two widely used test problem benchmarks. Our experimental results show that the proposed model outperforms established MOEAs at solving most of the test problems.
