An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

Mi Li, Huan Chen, Xiaodong Wang, Ning Zhong and Shengfu Lu
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Mi Li: Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, P. R. China
Huan Chen: Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, P. R. China
Xiaodong Wang: Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, P. R. China
Ning Zhong: Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, P. R. China‡Maebashi Institute of Technology, 460 Kamisa-cho, Maebashi-shi, Gunma Prefecture 3700816, Japan
Shengfu Lu: Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 03, 833-866

Abstract: The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

Keywords: Particle swarm optimization; adaptive inertia weight; mutation threshold; mutation; diversity (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0219622019500147

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