R2-Based Multi/Many-Objective Particle Swarm Optimization.

Abstract:

:We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA.

journal_name

Comput Intell Neurosci

authors

Díaz-Manríquez A,Toscano G,Barron-Zambrano JH,Tello-Leal E

doi

10.1155/2016/1898527

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

1898527

eissn

1687-5265

issn

1687-5273

journal_volume

2016

pub_type

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