Abstract
Particle swarm optimization (PSO) has been developing at a fast pace and has a wide range of applications since it is easy to understand and implement. However, it is greatly limited by the problem of being trapped in local optimum. Inspired by the migration behavior of animals, MBPSO is proposed. In MBPSO, the whole evolutionary cycle is divided into several increasing sub-evolutionary cycles. As the particles complete each sub-evolutionary cycle, they move around from their original position. Moreover, in order to ensure convergence and improve con-vergence speed, acceleration coefficient and inertia weight change asynchronously with time. Standard PSO, SAPSO and SecPSO are selected for comparison with MBPSO. Then the performance of the four algorithms in six functions is tested. It is ultimately proved that the proposed MBPSO algorithm is more effective than the other three PSO algorithms.
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Acknowledgement
Jingzhou Jiang and Shukun Jiang contributed equally to this paper and shared the first authorship. This work is partially supported by the Natural Science Foundation of Guangdong Province (2016A030310074), Project supported by Innovation and Entrepreneurship Research Center of Guangdong University Student (2018A073825), Research Cultivation Project from Shenzhen Institute of Information Technology (ZY201717) and Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (2017GWTSCX0 38). The author is sincerely grateful to all those who gave advice and discussions, especially Ben Niu. In addition, the author also wants to express sincere thanks to Qianying Liu who provided valuable advice.
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Jiang, S., Jiang, J., Zheng, C., Liang, Y., Tan, L. (2019). An Improved PSO Algorithm with Migration Behavior and Asynchronous Varying Acceleration Coefficient. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_59
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DOI: https://doi.org/10.1007/978-3-030-26766-7_59
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