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

Autor:	Zhiteng Ma, Xianfeng Yuan, Sen Han, Deyu Sun, Yan Ma
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	particle swarm optimizer metaheuristic nonlinear dynamic weights dynamic learning factors numerical optimization functions Mathematics QA1-939
Zdroj:	Symmetry, Vol 11, Iss 7, p 876 (2019)
Druh dokumentu:	article
ISSN:	2073-8994
DOI:	10.3390/sym11070876
Popis:	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.
Databáze:	Directory of Open Access Journals
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