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 Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Díaz-Manríquez A,Toscano G,Barron-Zambrano JH,Tello-Leal Edoi
10.1155/2016/1898527subject
Has Abstractpub_date
2016-01-01 00:00:00pages
1898527eissn
1687-5265issn
1687-5273journal_volume
2016pub_type
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