Abstract
In conventional particle swarm optimization (PSO) algorithm, each particle adjusts their position and velocity to achieve an optimal solution by iteration, but it has the tendency to fall into the local optimum. In order to avoid the classic PSO problem, a new variant of PSO exemplar based on non-directional learning strategy (NLS) is introduced in this paper, which uses random information of partial dimension of personal best experience in every iteration. Initially, the above method randomly extracts dimensional experience from all dimensions of personal best position of particles. Then, the non-directional position is generated by information of random-dimension. Based on the above mechanism, particles are set to obtain information from personal, population and non-directional position, which can enhance particles search ability. Non-directional learning strategy PSO is tested by several benchmark functions, along with some novel PSO algorithms, and the results illustrate that the convergence accuracy is improved significantly.
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Acknowledge
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 (2017GWTSCX038). And the authors appreciate everyone who provided us with constructive suggestions and discussions, especially Professor Ben Niu and Ms. Lulu Zuo.
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Ye, Z., Li, C., Liang, Y., Chen, Z., Tan, L. (2019). Non-directional Learning Strategy Particle Swarm Optimization Algorithm. 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_55
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DOI: https://doi.org/10.1007/978-3-030-26766-7_55
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