Abstract
This work presents a new optimization technique called dual species conservation particle swarm optimization (DSPSO) for finding multiple optima (global or local) of multimodal functions. The basis of the proposed algorithm is repeatedly using species conservation and hill-valley detecting mechanism to refine the species set. To improve the balance between exploration and exploitation of the standard Particle Swarm Optimization (PSO), a local search around found optima strategy is adopted in PSO. The performance of DSPSO is validated on a set of widely used multimodal benchmark functions. Numerical results show that the proposed technique is effective and efficient in finding multiple solutions of selected benchmark.
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References
Beasley, D., Bull, D.R., Martin, R.R.: A Sequential Niche Technique for Multimodal Function Optimization. Evolutionary Computation 1(2), 101–125 (1993)
Cho, H., Kim, D., Olivera, F., Guikema, S.D.: Enhanced speciation in particle swarm optimization for multi-modal problems. European Journal of Operational Research 213(1), 15–23 (2011)
Das, S., Maity, S., Qu, B.Y., Suganthan, P.: Real-parameter evolutionary multimodal optimization–A survey of the state-of-the-art. Swarm and Evolutionary Computation 1(2), 71–88 (2011)
Della Cioppa, A., De Stefano, C., Marcelli, A.: Where Are the Niches? Dynamic Fitness Sharing. IEEE Transactions on Evolutionary Computation 11(4), 453–465 (2007)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kharma, N.: On Clustering in Evolutionary Computation. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 1752–1759 (2006)
Li, J.P., Balazs, M.E., Parks, G.T., Clarkson, P.J.: A species conserving genetic algorithm for multimodal function optimization. Evolutionary Computation 10(3), 207–234 (2002)
Li, J.P., Li, X.D., Wood, A.: Species based evolutionary algorithms for multimodal optimization: A brief review. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Li, M., Lin, D., Kou, J.: A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization. Applied Soft Computing 12(3), 975–987 (2012)
Shen, D., Li, Y., Wei, B., Xia, X.: Adaptive Forking Multipopulation Differential Evolution Algorithm for Multimodal Optimization. Journal of Convergence Information Technology 7(5), 57–65 (2012)
Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: Proceedings of the 2004 Congress on Evolutionary Computation, vol. 2, pp. 1382–1389 (2004)
Ursem, R.: Multinational evolutionary algorithms. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1633–1640 (1999)
Ursem, R.: Multinational GAs: Multimodal optimization techniques in dynamic environments. In: Proc. of the Genetic and Evolutionary Computation Conference, pp. 19–26 (2000)
Vitela, J.E., Castaños, O.: A sequential niching memetic algorithm for continuous multimodal function optimization. Applied Mathematics and Computation (2012), doi:10.1016/j.amc.2011.05.051
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Shen, D., Xia, X. (2012). A Local Search Particle Swarm Optimization with Dual Species Conservation for Multimodal Optimization. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_51
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DOI: https://doi.org/10.1007/978-3-642-34062-8_51
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