DocumentCode :
3262343
Title :
A decomposition-based multi-objective Particle Swarm Optimization algorithm for continuous optimization problems
Author :
Peng, Wei ; Zhang, Qingfu
Author_Institution :
Sch. of Comput., Nat. Univ. of Defense Technol., Changsha
fYear :
2008
fDate :
26-28 Aug. 2008
Firstpage :
534
Lastpage :
537
Abstract :
Particle swarm optimization (PSO) is a heuristic optimization technique that uses previous personal best experience and global best experience to search global optimal solutions. This paper studies the application of PSO techniques to multi-objective optimization using decomposition methods. A new decomposition-based multi-objective PSO algorithm is proposed, called MOPSO/D. It integrates PSO into a multiobjective evolutionary algorithm based on decomposition (MOEA/D). The experimental results demonstrate that MOPSO/D can achieve better performance than a well-known MOEA, NSGA-II with differential evolution (DE), on most of the selected test instances. It shows that MOPSO/D will be a competitive candidate for multi-objective optimization.
Keywords :
evolutionary computation; particle swarm optimisation; search problems; continuous optimization problems; decomposition methods; global best experience; global optimal solution searching; heuristic optimization technique; multiobejective evolutionary algorithm; multiobjective particle swarm optimization algorithm; personal best experience; Artificial intelligence; Data structures; Evolutionary computation; Optimization methods; Pareto optimization; Particle swarm optimization; Search methods; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-2512-9
Electronic_ISBN :
978-1-4244-2513-6
Type :
conf
DOI :
10.1109/GRC.2008.4664724
Filename :
4664724
Link To Document :
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