Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization
In this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201818903020 |
| _version_ | 1818731996109602816 |
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| author | Jong Choi Tae Lee Yeonju |
| author_facet | Jong Choi Tae Lee Yeonju |
| author_sort | Jong Choi Tae |
| collection | DOAJ |
| description | In this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE algorithm uses a slow mutation strategy so that its convergence speed is slow compared to several state-of-the-art DE algorithms. The asynchronous method is one of the recently investigated approaches that if it finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual. Therefore, it can improve the convergence speed significantly. We evaluated the optimization performance of A-jDE algorithm in 13 scalable benchmark problems on 30 and 100 dimensions. Our experiments prove that incorporating jDE algorithm with the asynchronous method can improve the optimization performance in not only a unimodal benchmark problem but also multimodal benchmark problem significantly. |
| first_indexed | 2024-12-17T23:26:33Z |
| format | Article |
| id | doaj.art-ad64f289af684891af71b83e58b5dff6 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-17T23:26:33Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-ad64f289af684891af71b83e58b5dff62022-12-21T21:28:45ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-011890302010.1051/matecconf/201818903020matecconf_meamt2018_03020Asynchronous differential evolution with selfadaptive parameter control for global numerical optimizationJong Choi TaeLee YeonjuIn this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE algorithm uses a slow mutation strategy so that its convergence speed is slow compared to several state-of-the-art DE algorithms. The asynchronous method is one of the recently investigated approaches that if it finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual. Therefore, it can improve the convergence speed significantly. We evaluated the optimization performance of A-jDE algorithm in 13 scalable benchmark problems on 30 and 100 dimensions. Our experiments prove that incorporating jDE algorithm with the asynchronous method can improve the optimization performance in not only a unimodal benchmark problem but also multimodal benchmark problem significantly.https://doi.org/10.1051/matecconf/201818903020 |
| spellingShingle | Jong Choi Tae Lee Yeonju Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization MATEC Web of Conferences |
| title | Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization |
| title_full | Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization |
| title_fullStr | Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization |
| title_full_unstemmed | Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization |
| title_short | Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization |
| title_sort | asynchronous differential evolution with selfadaptive parameter control for global numerical optimization |
| url | https://doi.org/10.1051/matecconf/201818903020 |
| work_keys_str_mv | AT jongchoitae asynchronousdifferentialevolutionwithselfadaptiveparametercontrolforglobalnumericaloptimization AT leeyeonju asynchronousdifferentialevolutionwithselfadaptiveparametercontrolforglobalnumericaloptimization |