Abstract
Swarm intelligence algorithm (SI) is a kind of stochastic search algorithm based on swarm. Similar to other evolutionary algorithm, when solving the complicated multimodal problem using SI, it is easy to have premature convergence. So, to promote the optimization of swarm intelligence algorithm, the typical algorithm (Particle swarm optimizer) of swarm intelligence algorithm is selected to explore some strategies how to improve the performance. In this paper, we explore the follow research: firstly, the mutation operation is introduced to produce new learn example for each individual in itself evolution process; secondly, in the view of the idea of simulated annealing, the range strategy of fitness of each individual is proposed; finally, to make best use of each individual information, the comprehensive learning strategy is adopted to improve each individual evolution mechanism.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grants nos. 71461027, 71471158). Science and technology talent training object of Guizhou province outstanding youth (Qian ke he ren zi [2015] 06). Guizhou province natural science foundation in China (Qian Jiao He KY [2014] 295); 2013, 2014 and 2015 Zunyi 15851 talents elite project funding; Zhunyi innovative talent team (Zunyi KH (2015) 38); Project of teaching quality and teaching reform of higher education in Guizhou province (Qian Jiao gaofa [2013] 446, [2015] 337), College students’ innovative entrepreneurial training plan (201410664004, 201510664016); Guizhou science and technology cooperation plan (Qian Ke He LH zi [2015] 7050, [2015] 7005, [2016] 7028, Qian Ke He J zi LKZS [2014] 30); Zunyi Normal College Research Funded Project (2012 BSJJ19).
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Liu, Y., Li, C., Zeng, Q., Zhang, Z., Liu, R., Huang, T. (2016). Effects of Simulated Annealing Strategy on Swarm Intelligence Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_66
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DOI: https://doi.org/10.1007/978-3-319-42291-6_66
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