Abstract
A double particle swarm optimization (DPSO), in which MPSO proposed by Sun et al. [1] is used as a global search algorithm and PSO with feasibility-based rules is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems. MPSO can solve the non-continuous variables very well. However, the imprecise values of continuous variables brought the inconsistent results of each run. A particle swarm optimization with feasibility-based rules is proposed to find optimal values of continuous variables after the MPSO algorithm finishes each independent run, in order to obtain the consistent optimal results for mixed-variable optimization problems. The performance of DPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.
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Sun, C., Zeng, J., Pan, J., Chu, S., Zhang, Y. (2011). A Double Particle Swarm Optimization for Mixed-Variable Optimization Problems. In: Katarzyniak, R., Chiu, TF., Hong, CF., Nguyen, N.T. (eds) Semantic Methods for Knowledge Management and Communication. Studies in Computational Intelligence, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23418-7_9
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DOI: https://doi.org/10.1007/978-3-642-23418-7_9
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