Abstract
Particle swarm optimization with passive congregation (PSOPC) is a novel variant of particle swarm optimization (PSO) by simulating the animal congregation phenomenon. Although it is superior to the standard version in some cases, however, due to the randomly selected neighbor particle, the performance of PSOPC is not always stable. Therefore, in this paper, a new variant – nearest neighbor interaction particle swarm optimization based on small world model (NNISW) is designed to solve this problem. In NNISW, the additional congregation item is associated with the best particle, nor the random ones, and the small world topology structure is introduced also to simulate the true swarm behavior. After compared with other seven famous benchmarks in high-dimensional cases, the performance of this new variant is superior to other three variants of PSO.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)
Chen, S., Hong, X., Luk, B.L., Harris, C.J.: Non-linear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation 1(4), 246–258 (2009)
Senthil Arumugam, M., Ramana Murthy, G., Loo, C.K.: On the optimal control of the steel annealing processes as a two-stage hybrid systems via PSO algorithms. International Journal of Bio-Inspired Computation 1(3), 198–209 (2009)
Parsopoulos, K.E., Kariotou, F., Dassios, G., Vrahatis, M.N.: Tackling magnetoencephalography with particle swarm optimization. International Journal of Bio-Inspired Computation 1(1/2), 32–49 (2009)
Cui, Z.H., Zeng, J.C., Sun, G.J.: A fast particle swarm optimization. International Journal of Innovative Computing, Information and Control 2(6), 1365–1380 (2006)
Monson, C.K., Seppi, K.D.: The Kalman swarm: a new approach to particle motion in swarm optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 140–150. Springer, Heidelberg (2004)
Iwasaki, N., Yasuda, K.: Adaptive particle swarm optimization using velocity feedback. International Journal of Innovative Computing, Information and Control 1(3), 369–380 (2005)
Liang, J., Qin, A., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Cui, Z.H., Zeng, J.C.: A guaranteed global convergence particle swarm optimizer. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 762–767. Springer, Heidelberg (2004)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm opitmizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation 8(3), 240–255 (2004)
He, S., Wu, Q., et al.: A particle swarm optimizer with passive congregation. BioSystems 78(1/3), 135–147 (2004)
Watts, D., Strogatz, S.: Collective dynamics of ”small-world” networks. Nature 393(6684), 440–442 (1998)
Newman, M., Watts, D.: Renormalization group analysis of the small-world network model. Physics Letter A, 263(4/6), 341–346 (1999)
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Cui, Z., Chu, Y., Cai, X. (2009). Nearest Neighbor Interaction PSO Based on Small-World Model. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_77
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DOI: https://doi.org/10.1007/978-3-642-04394-9_77
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