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.
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Huo, X., Shen, L., Zhu, H. (2006). A Smart Particle Swarm Optimization Algorithm for Multi-objective Problems. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_8
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DOI: https://doi.org/10.1007/11816102_8
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