Abstract
A hybrid intelligent algorithm (HIA), in which MPSO with feasibility-based rules proposed by Sun et al. [1] is used as a global search algorithm and simulator annealing (SA) is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems. MPSO can obtain the values of the non-continuous variables very well for mixed-variable optimization problems. However, the imprecise values of continuous variables brought the inconsistent results of each run. A simulator annealing algorithm 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 HIA 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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Yunqiang, Z., Ying, T., Chaoli, S., Jianchao, Z. (2012). A Hybrid Intelligent Algorithm for Mixed-Variable Optimization Problems. In: Zhang, Y. (eds) Future Communication, Computing, Control and Management. Lecture Notes in Electrical Engineering, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27311-7_33
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DOI: https://doi.org/10.1007/978-3-642-27311-7_33
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