Abstract
Particle Swarm Optimization (PSO) offers efficient simultaneous global and local searches but is challenged with the problem of slow local convergence. To address this issue, a hybrid comprehensive learning PSO algorithm with adaptive starting local search (ALS-HCLPSO) is proposed. Determining when to start local search is the main of ALS-HCLPSO. A quasi-entropy index is innovatively utilized as the criterion of population diversity to depict an aggregation degree of particles and to ascertain whether the global optimum basin has been explored. This adaptive strategy ensures the proper starting of local search. The test results on eight multimodal benchmark functions demonstrate the performance superiority of ALS-HCLPSO. And comparison results on six advanced PSO variants further test the validity and superiority of ALS-HCLPSO algorithm.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Grants No. 61571336, No. 61603280 and No. 71672137).
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Cao, Y., Li, W., Chaovalitwongse, W.A. (2017). Hybrid Comprehensive Learning Particle Swarm Optimizer with Adaptive Starting Local Search. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_16
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DOI: https://doi.org/10.1007/978-3-319-61824-1_16
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