DocumentCode :
579781
Title :
Self-Controlling Dominance Particle Swarm Optimization
Author :
Castro, Olacir R. ; Britto, A. ; Pozo, A.
Author_Institution :
Fed. Univ. of Parana, Curitiba, Brazil
fYear :
2012
fDate :
20-25 Oct. 2012
Firstpage :
172
Lastpage :
177
Abstract :
A well-known problem faced by Pareto Based Multi Objective Optimization Algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO) algorithms is the deterioration of its search ability when the number of objectives scales up. In the literature some techniques were proposed to overcome these limitations, between them the modification of the domination relation. This work aims to propose a new MOPSO algorithm called SCDAS-SMPSO which uses the Self-Controlling Dominance Area of Solutions (S-CDAS) technique to modify the domination relation and increase the selection pressure needed in many-objective optimization. The new MOPSO is compared through experiments with other two MOPSOs in terms of convergence and diversity in many-objective scenarios. The results were analyzed through different quality indicators and statistical tests.
Keywords :
Pareto optimisation; convergence; particle swarm optimisation; search problems; statistical analysis; MOPSO algorithm; Pareto based multiobjective optimization algorithms; SCDAS-SMPSO; domination relation modification; many-objective optimization; multiobjective particle swarm optimization algorithms; quality indicators; search ability; selection pressure; self-controlling dominance area-of-solutions technique; self-controlling dominance particle swarm optimization; statistical tests; Aerospace electronics; Convergence; Optimization; Particle swarm optimization; Search problems; Silicon; Vectors; CDAS; MOPSO; Many Objective; PSO; S-CDAS;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (SBRN), 2012 Brazilian Symposium on
Conference_Location :
Curitiba
ISSN :
1522-4899
Print_ISBN :
978-1-4673-2641-4
Type :
conf
DOI :
10.1109/SBRN.2012.28
Filename :
6374844
Link To Document :
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