Multiobjective Particle Swarm Optimization Without the Personal Best

doi:10.1007/s12204-014-1484-8

Abstract

Abstract: The personal best is an interesting topic, but little work has focused on whether it is still efficient for multiobjective particle swarm optimization. In dealing with single objective optimization problems, a single global best exists, so the personal best provides optimal diversity to prevent premature convergence. But in multiobjective optimization problems, the diversity provided by the personal best is less optimal, whereas the global archive contains a series of global bests, thus provides optimal diversity. If the algorithm excluding the personal best provides sufficient randomness, the personal best becomes worthless. Therefore we propose no personal best strategy that no longer uses the personal best when the global archive exceeds the population size. Experimental results validate the efficiency of our strategy.

Key words: multiobjective optimization problems| particle swarm optimization (PSO)| personal best| global best| global archive

摘要： The personal best is an interesting topic, but little work has focused on whether it is still efficient for multiobjective particle swarm optimization. In dealing with single objective optimization problems, a single global best exists, so the personal best provides optimal diversity to prevent premature convergence. But in multiobjective optimization problems, the diversity provided by the personal best is less optimal, whereas the global archive contains a series of global bests, thus provides optimal diversity. If the algorithm excluding the personal best provides sufficient randomness, the personal best becomes worthless. Therefore we propose no personal best strategy that no longer uses the personal best when the global archive exceeds the population size. Experimental results validate the efficiency of our strategy.

关键词: multiobjective optimization problems| particle swarm optimization (PSO)| personal best| global best| global archive

CLC Number:

TP 181

WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣). Multiobjective Particle Swarm Optimization Without the Personal Best[J]. Journal of shanghai Jiaotong University (Science), 2014, 19(2): 155-159.

References

[1] Kennedy J, Eberhart R. Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ, USA: IEEE,1995: 1942-1948.
[2] Xu H M, Wang Y L, Xu X. The crowd framework for multiobjective particle swarm optimization[DB/OL]. (2012-07-04) [2013-01-20].(http://link.springer.com/article/10.1007/s10462-012-9347/x)
[3] Xu H M, Wang Y L, Xu X. Multiobjective particle swarm optimization based on dimensional update [J].International Journal on Artificial Intelligence Tools,2013, 22(3): 1350015-1-1350015-27.
[4] Xu H M, Wang Y L, Xu X. Dominating global best selection for multi-objective particle swarm optimization [C]// Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering.Paris, France: Atlantis Press, 2013.
[5] Janson S, Merkle D. A new multi-objective particle swarm optimization algorithm using clustering applied to automated docking [C]//Proceedings of the Second International Conference on Hybrid Metaheuristics.Berlin: Springer-Verlag, 2005: 128-141.
[6] Branke J, Mostaghim S. About selecting the personal best in multi-objective particle swarm optimization[J]. Proceedings of 9th International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, 2006, 4193: 523-532.
[7] Ho S L, Yang S, Ni G, et al. A particle swarm optimization-based method for multiobjective design optimizations [J]. IEEE Transactions on Magnetics,2005, 41(5): 1756-1759.
[8] Mendes R, Kennedy J, Neves J. The fully informed particle swarm: Simpler, maybe better [J].IEEE Transactions on Evolutionary Computation,2004, 8(3): 204-210.
[9] Tsai S J, Sun T Y, Liu C C, et al. An improved multi-objective particle swarm optimizer for multiobjective problems [J]. Expert Systems with Applications,2010, 37(8): 5872-5886.
[10] Zhao S Z, Suganthan P N. Two-lbests based multiobjective particle swarm optimizer [J]. Engineering Optimization, 2011, 43(1): 1-17.
[11] Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation,2004, 8(3): 256-279.
[12] Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: Empirical results[J]. Evolutionary Computation, 2000, 8(2): 173-195.
[13] Deb K, Thiele L, Laumanns M, et al. Scalable test problems for evolutionary multiobjective optimization[M]//Abraham A, Jain L, Goldberg R. Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, Berlin: Springer, 2005: 105-145.
[14] Huband S, Barone L, While L, et al. A scalable multi-objective test problem toolkit [C]// Proceedings of 3rd Evolutionary Multiobjective Optimization, Lecture Notes in Computer Science. Berlin: Springer,2005: 280-295.
[15] Zitzler E, Brockhoff D, Thiele L. The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration [C]//Proceedings of 4th International Conference on Evolutionary Multi-criterion Optimization, Lecture Notes in Computer Science. Berlin: Springer, 2007:862-876.