A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://www.atlantis-press.com/article/125913568/view |
| _version_ | 1811291225866633216 |
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| author | Miguel Leon Ning Xiong Daniel Molina Francisco Herrera |
| author_facet | Miguel Leon Ning Xiong Daniel Molina Francisco Herrera |
| author_sort | Miguel Leon |
| collection | DOAJ |
| description | Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that MDEALS not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE. |
| first_indexed | 2024-04-13T04:26:07Z |
| format | Article |
| id | doaj.art-b80043dd64e84bd691063bbcbb53125c |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T04:26:07Z |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-b80043dd64e84bd691063bbcbb53125c2022-12-22T03:02:32ZengSpringerInternational Journal of Computational Intelligence Systems1875-688310.2991/ijcis.d.190711.001A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local SearchMiguel LeonNing XiongDaniel MolinaFrancisco HerreraDifferential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that MDEALS not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE.https://www.atlantis-press.com/article/125913568/viewDifferential evolutionL-SHADEMemetic algorithmAlopexLocal searchOptimization |
| spellingShingle | Miguel Leon Ning Xiong Daniel Molina Francisco Herrera A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search International Journal of Computational Intelligence Systems Differential evolution L-SHADE Memetic algorithm Alopex Local search Optimization |
| title | A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search |
| title_full | A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search |
| title_fullStr | A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search |
| title_full_unstemmed | A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search |
| title_short | A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search |
| title_sort | novel memetic framework for enhancing differential evolution algorithms via combination with alopex local search |
| topic | Differential evolution L-SHADE Memetic algorithm Alopex Local search Optimization |
| url | https://www.atlantis-press.com/article/125913568/view |
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