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Computational Intelligence and Neuroscience
Volume 2016, Article ID 1898527, 10 pages
Research Article

-Based Multi/Many-Objective Particle Swarm Optimization

1Facultad de Ingeniería y Ciencias, Universidad Autónoma de Tamaulipas, 87000 Victoria, TAMPS, Mexico
2Cinvestav Tamaulipas, Km. 5.5 Carretera Ciudad Victoria-Soto La Marina, 87130 Victoria, TAMPS, Mexico

Received 7 November 2015; Revised 26 January 2016; Accepted 16 February 2016

Academic Editor: Ezequiel López-Rubio

Copyright © 2016 Alan Díaz-Manríquez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose to couple the 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 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.