Pareto dominance-based MOEAs on Problems with Difficult Pareto Set Topologies

Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its re...

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Detalles Bibliográficos
Autores: SAUL ZAPOTECAS MARTINEZ, KIYOSHI TANAKA
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2018
País:México
Institución:Universidad Autónoma Metropolitana
Repositorio:Concentración de Recursos de Información Científica y Académica, UAM Cuajimalpa
Idioma:inglés
OAI Identifier:oai:ilitia.cua.uam.mx:123456789/450
Acceso en línea:http://ilitia.cua.uam.mx:8080/jspui/handle/123456789/450
Access Level:acceso abierto
Palabra clave:info:eu-repo/classification/cti/7
Algoritmos computacionales
Optimización matemática
Descripción
Sumario:Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.