Abstract
Heterogeneous Multi-Population Cultural Algorithm (HMP-CA) is one of the most recent architecture proposed to implement Multi-Population Cultural Algorithms which incorporates a number of heterogeneous local Cultural Algorithms (CAs) communicating with each other through a shared belief space. The heterogeneous local CAs are designed to optimize different subsets of the dimensions of a given problem. In this article, two dynamic dimension decomposition techniques are proposed including the top-down and bottom-up approaches. These dynamic approaches are evaluated using a number of well-known benchmark numerical optimization functions and compared with the most effective and efficient static dimension decomposition methods. The comparison results reveals that the proposed dynamic approaches are fully effective and outperforms the static approaches in terms of efficiency.
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References
Guo, Y.N., Cheng, J., Cao, Y.Y., Lin, Y.: A novel multi-population cultural algorithm adopting knowledge migration. Soft Computing 15(5), 897–905 (2011)
Lin, C.J., Chen, C.H., Lin, C.T.: A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(1), 55–68 (2009)
Mezura-Montes, E., Velazquez-Reyes, J., Coello Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 485–492 (2006)
Raeesi N., M.R., Chittle, J., Kobti, Z.: A new dimension division scheme for heterogenous multi-population cultural algorithm. In: The 27th Florida Artificial Intelligence Research Society Conference (FLAIRS-27), Pensacola Beach, FL, USA, May 21-23 (2014)
Raeesi N., M.R., Kobti, Z.: A knowledge-migration-based multi-population cultural algorithm to solve job shop scheduling. In: The 25th Florida Artificial Intelligence Research Society Conference (FLAIRS-25), pp. 68–73 (2012)
Raeesi N., M.R., Kobti, Z.: A multiagent system to solve JSSP using a multi-population cultural algorithm. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS, vol. 7310, pp. 362–367. Springer, Heidelberg (2012)
Raeesi N., M.R., Kobti, Z.: Heterogeneous multi-population cultural algorithm. In: IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, pp. 292–299 (2013)
Reynolds, R.G.: An introduction to cultural algorithms. In: Sebald, A.V., Fogel, L.J. (eds.) Thirs Annual Conference on Evolutionary Programming, pp. 131–139. World Scientific, River Edge (1994)
Schutze, O., Talbi, E.G., Coello Coello, C., Santana-Quintero, L.V., Pulido, G.T.: A memetic PSO algorithm for scalar optimization problems. In: IEEE Swarm Intelligence Symposium (SIS), Honolulu, HI, USA, April 1-5, pp. 128–134 (2007)
Xu, W., Zhang, L., Gu, X.: A novel cultural algorithm and its application to the constrained optimization in ammonia synthesis. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds.) LSMS 2010. CCIS, vol. 98, pp. 52–58. Springer, Heidelberg (2010)
Yu, W.J., Zhang, J.: Multi-population differential evolution with adaptive parameter control for global optimization. In: Genetic and Evolutionary Computation Conference (GECCO), Dublin, Ireland, pp. 1093–1098 (2011)
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Raeesi N., M.R., Kobti, Z. (2014). Heterogeneous Multi-Population Cultural Algorithm with a Dynamic Dimension Decomposition Strategy. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_36
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DOI: https://doi.org/10.1007/978-3-319-06483-3_36
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