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Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China
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Symmetry 2019, 11(7), 876; https://doi.org/10.3390/sym11070876
Received: 4 June 2019 / Revised: 30 June 2019 / Accepted: 3 July 2019 / Published: 3 July 2019
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Abstract

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms. View Full-Text
Keywords: particle swarm optimizer; metaheuristic; nonlinear dynamic weights; dynamic learning factors; numerical optimization functions particle swarm optimizer; metaheuristic; nonlinear dynamic weights; dynamic learning factors; numerical optimization functions
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Ma, Z.; Yuan, X.; Han, S.; Sun, D.; Ma, Y. Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization. Symmetry 2019, 11, 876.

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