Abstract
In recent years, multi-layer particle swarm optimization (MLPSO) has been applied in various global optimization problems for its superior performance. However, fast convergence speed leads the algorithm easy to converge to the local minimum. Therefore, MLPSO-Powell algorithm is proposed in this paper, selecting several swarm particles by the tournament operator in each generation to run Powell algorithm. MLPSO global searching performance with Powell local searching performance forces swarm particles to search more optima as much as possible, then it will rapidly converge as soon as it gets close to the global optimum. MLPSO-Powell enhances local search ability of PSO in dealing with multi-modal problems. The experimental results shows that the proposed approach improves performance and final results.
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Acknowledgments
This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 61373054, No. 61472164, No. 61472163, No. 61672262, No. 61640218. Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025. Science and technology project of Shandong Province under Grant No. 2015GGX101025. Project of Shandong Province Higher Educational Science and Technology Program under Grant No. J16LN07. Shandong Provincial Key R&D Program under Grant No. 2016ZDJS01A12.
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Sun, F. et al. (2017). Improving Multi-layer Particle Swarm Optimization Using Powell Method. 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_18
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DOI: https://doi.org/10.1007/978-3-319-61824-1_18
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