Abstract
In order to keep balance of premature convergence and diversity maintenance, an AntiCentroid-oriented particle updating strategy and an improved Particle Swarm Algorithm (ACoPSA) are presented in this paper. The swarm centroid reflects the search focus of the PSA algorithm and its distance to the global best particle (gbest) indicates the behavior difference between the population search and the gbest. Therefore the directional vector from the swarm centroid to the gbest implies an effective direction that particles should follow. This direction is utilized to update the particle velocity and to guide swarm search. Experimental comparisons among ACoPSA, standard PSA and a recent perturbed PSA are made to validate the efficacy of the strategy. The experiments confirm us that the swarm centroid-guided particle updating strategy is encouraging and promising for stochastic heuristic algorithms.
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References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks IV, pp. 1942–1948. IEEE, Piscataway (1995)
Yao, J., Kharma, N., Grogono, P.: Bi-objective Multipopulation Genetic Algorithm for Multimodal Function Optimization. IEEE Trans. on Evolutionary Computation 14(1), 80–102 (2010)
Chen, W.-N., Zhang, J., Chung, H.S.H., Zhong, W.-L., Wu, W.-G., Shi, Y.-H.: A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems. IEEE Trans. on Evolutionary Computation 14(2), 278–300 (2010)
Liu, J., Zhong, W.C., Jiao, L.C.: A Multiagent Evolutionary Algorithm for Combinatorial Optimization Problems. IEEE Transactions on Systems, Man and Cybernetics-Part B 40(1), 229–240 (2010)
Wu, Q.D., Wang, L.: Intelligent Particle Swarm Optimization Algorithm Research and Application. Jiangsu Education Press, Nanjing (2005)
Zhao, X.C.: A perturbed particle swarm algorithm for numerical optimization. Applied Soft Computing 10, 119–124 (2010)
Hu, W., Li, Z.S.: A Simpler and More Effective Particle Swarm Optimization Algorithm. Journal of Software 18(4), 861–868 (2007)
Hu, J.X., Zeng, J.C.: A Two-Order Particle Swarm Optimization Model. Journal of Computer Research and Development 44(11), 1825–1831 (2007)
Ji, Z., Zhou, J.R., Liao, H.L., Wu, Q.H.: A Novel Intelligent Single Particle Optimizer. Chinese Journal of Computers 33(3), 556–561 (2010)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Zhan, Z.-H., Zhang, J., Li, Y., Chung, H.S.-H.: Adaptive Particle Swarm Optimization. IEEE Transactions on Systems, Man and Cybernetics-Part B 39(6), 1362–1381 (2009)
Zhao, X.C., Hao, J.L.: Exploration/exploitation tradeoff with cell-shift and heuristic crossover for evolutionary algorithms. Journal of Systems Science and Complexity 20(1), 66–74 (2007)
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Zhao, X., Wang, W. (2010). An AntiCentroid-oriented Particle Swarm Algorithm for Numerical Optimization. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16527-6_38
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DOI: https://doi.org/10.1007/978-3-642-16527-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16526-9
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