Abstract
The classical particle swarm optimization (PSO) trains the particles to move toward the global best particle in every iteration. So, it has a great possibility of being trapped into local optima. To deal with this issue, this paper improves the learning strategy of PSO. Therefore, an area-oriented particle swarm optimization (AOPSO) is proposed, which contributes to leading the particles to move toward an area surrounded by some suboptimal particles besides the best one. 10 test functions are employed to compare the performance of AOPSO with the classical PSO and 3 other improved PSOs. AOPSO performs the best in 5 test functions and relatively better than some of the other algorithms in the rest, which sufficiently demonstrates the effectiveness of AOPSO.
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Acknowledgments
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. Churong Zhang.
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Liu, T., Chen, J., Rong, Y., Zheng, Y., Tan, L. (2019). An Improved PSO Algorithm with an Area-Oriented Learning Strategy. 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_58
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DOI: https://doi.org/10.1007/978-3-030-26766-7_58
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