Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization
| Autor: | Yan Ma, Xianfeng Yuan, Zhiteng Ma, Deyu Sun, Sen Han |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Optimization problem
Physics and Astronomy (miscellaneous) Computer science General Mathematics Chaotic Stability (learning theory) MathematicsofComputing_NUMERICALANALYSIS numerical optimization functions 02 engineering and technology metaheuristic Symmetric probability distribution Convergence (routing) 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Inverse trigonometric functions Metaheuristic lcsh:Mathematics Particle swarm optimization 020206 networking & telecommunications nonlinear dynamic weights lcsh:QA1-939 Chemistry (miscellaneous) particle swarm optimizer dynamic learning factors 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Symmetry Volume 11 Issue 7 Symmetry, Vol 11, Iss 7, p 876 (2019) |
| 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: | OpenAIRE |
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