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A decomposition-based multi-objective Particle Swarm Optimization algorithm for continuous optimization problems

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2 Author(s)
Wei Peng ; Sch. of Comput., Nat. Univ. of Defense Technol., Changsha ; Qingfu Zhang

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.

Published in:

Granular Computing, 2008. GrC 2008. IEEE International Conference on

Date of Conference:

26-28 Aug. 2008