Abstract
Similar with other swarm algorithms, the PSO algorithm also suffers from premature convergence. Mutation is a widely used strategy in the PSO algorithm to overcome the premature convergence. This paper discusses some induction patterns of mutation (IPM) and typical algorithms, and then presents a new PSO algorithm – the Limited Mutation PSO algorithm. Basing on a special PSO model depicted as “social-only”, the LMPSO adopts a new mutation strategy – limited mutation. When the distance between one particle and the global best location is less than a threshold predefined, some dimensions of the particles will mutate under specific rules. The LMPSO is compared to other five different types of PSO with mutation strategy, and the experiment results show that the new algorithm performances better on a four-function test suite with different dimensions.
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References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Conf.on Neural Networks, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway, NJ (1995)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway, NJ (1998)
Kennedy, J.: The particle swarm: Social adaptation of knowledge. In: IEEE International Conference on Evolutionary Computation, pp. 303–308. IEEE Computer Society Press, Los Alamitos (1997)
Pasupuleti, S.: The Gregarious Particle Swarm Optimizer(G-PSO). In: GECCO 2006, July 8-12, Seattle, Washington, USA (2006)
Xie, X.-F.: A Dissipative Particle Swarm Optimization. In: Congress on Evolutionary Computation (CEC), Hawaii, USA, pp. 1456–1461 (2002)
Riget, J.: A Diversity-Guided Particle Swarm Optimizer – the ARPSO
Ran, H.: An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity. Journal of Software (2005)
Jiao-Chao, Z.: A Guaranteed Global Convergence Particle Swarm Optimizer. Journal of Computer Research and Developemnt 41 (2004)
Zhen-su, L.: Particle Swarm Optimization with Adaptive Mutation 32(3) (March 2004)
Jiang-hong, H.: Adaptive Particle Swarm Optimization Algorithm and Simulation. Journal of System Simulation 18(10) (October 2006)
Hao-yang, W., Chang-chun, Z.: Adaptive Genetic Algorithm to Improve Group Premature Convergence. Journal of Xi’an Jiaotong University 33 (1999)
Ratnaweera, A., Halgamuge, S., Watson, H.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation 8, 240–255 (2004)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA, pp. 84–89. IEEE Computer Society Press, Los Alamitos (1998)
Lovbjerg, M., Rasmussen, T.K., Krink, T.: Hybrid Particle Swarm Optimizer with Breeding and Subpopulations. In: Proceeding of the third Genetic and Evolutionary Computation Conference (2001)
Suganthan, P.N.: Particle Swarm Optimizer with Neighborhood Operator. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1958–1962. IEEE Service Center, Piscataway, NJ (1999)
Kennedy, J.: Small worlds and Mega-minds: effects of neighborhood topology on particle swarm performance. In: Proc. Congress on Evolutionary Computation, 1931-1938, IEEE Service Center, Piscataway, NJ (1999)
Van den Bergh,: A New Locally Convergent Particle Swarm Optimizer. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, IEEE Computer Society Press, Los Alamitos (2002)
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Song, C., Zhao, H., Cai, W., Zhang, H., Zhao, M. (2007). The Limited Mutation Particle Swarm Optimizer. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_29
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DOI: https://doi.org/10.1007/978-3-540-74769-7_29
Publisher Name: Springer, Berlin, Heidelberg
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