Abstract
To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.
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References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Clerc, M.: Particle Swarm Optimization. ISTE Publishing Company (2006)
Ju, J., Wei, S.: Endowment versus Finance: A Wooden Barrel Theory of International Trade, CEPR Discussion Papers 5109, C.E.P.R. Discussion Papers (2005)
Xinchao, Z.: A perturbed particle swarm algorithm for numerical optimization. Applied Soft Computing 10, 119–124 (2010)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)
Roy, R., Dehuri, S., Cho, S.B.: A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem. International Journal of Applied Metaheuristic Computing 2(4), 41–57 (2012)
Chen, W., Zhang, J.: A novel set-based particle swarm optimization method for discrete optimization problem. IEEE Transactions on Evolutionary Computation 14, 278–300 (2010)
Kennedy, J., Clerc, M., et al.: Particle Swarm Central (2012), http://www.particleswarm.info/Programs.html
Mercer, R.E., Sampson, J.R.: Adaptive search using a reproductive metaplan. Kybernetes 7(3), 215–228 (1978)
Keane, A.J.: Genetic algorithm optimization in multi-peak problems: studies in convergence and robustness. Artificial Intelligence in Engineering 9(2), 75–83 (1995)
Pedersen, M.E.H., Chipperfield, A.J.: Simplifying particle swarm optimization. Applied Soft Computing 10(2), 618–628 (2010)
Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 11–18 (2002)
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Wang, Z., Sun, Y. (2013). Cask Theory Based Parameter Optimization for Particle Swarm Optimization. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_16
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DOI: https://doi.org/10.1007/978-3-642-38703-6_16
Publisher Name: Springer, Berlin, Heidelberg
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