| Autor: |
Mohamad Faiz Ahmad, Nor Ashidi Mat Isa, Wei Hong Lim, Koon Meng Ang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Alexandria Engineering Journal, Vol 61, Iss 12, Pp 11835-11858 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1110-0168 |
| DOI: |
10.1016/j.aej.2022.05.028 |
| Popis: |
Differential evolution (DE) is a popular optimization algorithm with easy implementation and fast convergence rate. For evolutionary algorithms such as DE, the initialization process of solution members is crucial because the distribution of initial population can govern the overall quality of final solution obtained in terms of accuracy and convergence speed. This study leverages the strengths of both chaotic maps and oppositional-based learning strategy to design a new DE variant with modified initialization scheme, namely chaotic oppositional DE (CODE) in order to generate the initial population with good quality of mean fitness and diversity of the solutions. The effectiveness of CODE variants incorporated with seven different chaotic maps are investigated using CEC 2014 benchmark functions and the chaotic circle oppositional DE (CCODE) is revealed as the best performing CODE variants. The optimization performance of CCODE is further compared with other existing optimization algorithms in terms of solution accuracy and convergence speed. Extensive simulation studies prove that the proposed algorithm is able to outperform its peers by achieving better trade-off between two contradicting requirements of fast convergence speed and population diversity preservation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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