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.
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Guevara-Souza, M., Vallejo, E.E. Using a simulated Wolbachia infection mechanism to improve multi-objective evolutionary algorithms. Nat Comput 14, 157–167 (2015). https://doi.org/10.1007/s11047-013-9404-7
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DOI: https://doi.org/10.1007/s11047-013-9404-7