Abstract
Previous work presented some modified approaches based particle swarm optimization (PSO) to solve complex optimization problems. Preliminary results demonstrated that PSO with crossover (CPSO) constituted a promising approach to solve some optimization problems. However how to optimize high dimensional problem with crossover became challenging. In this paper, a modified PSO with dimension crossover is proposed. First we analyze the cause of hardly optimizing the high dimensional problem, and then design one dynamic dimension crossover PSO (DDC-PSO) to cope with high dimensional problems. Theoretical analysis is also presented to show why the modified algorithm can be effective. Finally DDC-PSO is tested on five benchmark optimization problems and the results show a superior performance compared to the standard PSO and CPSO.
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Hu, C., Yan, X., Li, C. (2008). Particle Swarm Optimization with Dynamic Dimension Crossover for High Dimensional Problems. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_82
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DOI: https://doi.org/10.1007/978-3-540-92137-0_82
Publisher Name: Springer, Berlin, Heidelberg
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