Abstract
The effects of various neighborhood models on the particle swarm algorithm were investigated in this paper. We also gave some additional insight into the PSO neighborhood model selection topic. Our experiment results testified that the gbest model converges quickly on problem solutions but has a weakness for becoming trapped in local optima, while the lbest model converges slowly on problem solutions but is able to “flow around” local optima, as the individuals explore different regions. The gbest model is recommended strongly for unimodal objective functions, while a variable neighborhood model is recommended for multimodal objective functions.
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References
Kennedy J, Eberhart R. C (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on neural networks, 1942–1948
Eberhart R C, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: Proceedings of 7th annual conference on evolutionary computation, 611–616
Boeringer D W, Werner D H (2004) Particle swarm optimization versus genetic algorithms for phased array synthesis. IEEE Transactions on Antennas and Propagation, 52(3):771–779
Parsopoulos K E, Vrahatis M N (2002) Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 1:235–306
Sousa T, Silva A, Neves A (2003) A particle swarm data miner. In: Lecture Notes in Computer Science, Fernando M P, Salvador A (Eds.): EPIA 2003, LNAI 2902, Springer-Verlag, 43–53
Ting T, Rao M, Loo C K, Ngu S S (2003) Solving unit commitment problem using hybrid particle swarm optimization. Journal of Heuristics, 9:507–520
Chang B, Ratnaweera A, Halgamuge S (2004) Particle swarm optimisation for protein motif discovery. Genetic Programming and Evolvable Machines, 5:203–214
Kennedy J, Eberhart R (2001) Swarm Intelligence. Morgan Kaufmann Publishers, Inc., San Francisco, CA
Sugarthan P N 1998) Particle swarm optimiser with neighbourhood operator. In: Proceedings of IEEE International Conference on Evolutionary Computing, 3:1958–1964
Peer E S, van den Bergh F, Engelbrecht A P (2003) Using neighborhoods with the guaranteed convergence PSO. In: Proceeding of IEEE conference on Evolutionary Computation, 235–242
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceeding of IEEE conference on Evolutionary Computation, 1671–1676
Watts D J, Strogatz S H (1998) Collective dynamics of ‘Small-World’ networks. Nature, 393:40–442
Watts D J (1999) Small World: The dynamics of networks between order and randomness. Princeton University Press
Eberhart R C, Shi Y (2002) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of IEEE International Congress on Evolutionary Computation, 84–88
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(l):58–73
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Liu, H., Li, B., Ji, Y., Sun, T. (2006). Particle Swarm Optimisation from lbest to gbest. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_41
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DOI: https://doi.org/10.1007/3-540-31662-0_41
Publisher Name: Springer, Berlin, Heidelberg
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