A smart particle swarm optimization algorithm for multi-objective problems

Authors:
Xiaohua Huo;Lincheng Shen;Huayong Zhu
Affiliations:
Mechatronics and Automation School, National University of Defense Technology, Changsha, China;Mechatronics and Automation School, National University of Defense Technology, Changsha, China;Mechatronics and Automation School, National University of Defense Technology, Changsha, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

Citing 3
Cited 0

Particle swarm optimization method in multiobjective problems

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Multiobjective optimization using dynamic neighborhood particle swarm optimization

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MOPSO: a proposal for multiple objective particle swarm optimization

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Abstract

Maintaining the diversity and convergence of Pareto optimal solutions is a desired task of optimization methods for multi-objective optimization problems(MOP). While accelerating the computing speed is important for algorithms to solve real-life MOP also. A Smart Particle Swarm Optimization algorithm for MOP(SMOPSO) is proposed. By setting the cooperative action of all the objective functions as the global best guide of swarm and selecting the closest or farthest archive member as the personal best guide of each particle, the SMOPSO method can find many Pareto optimal solutions in less iteration steps. Three well-known test functions have been used to validate our approach. Results show that the SMOPSO method is available and rapid.