A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search Article Swipe
Miguel León
,
Ning Xiong
,
Daniel Molina
,
Francisco Herrera
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.2991/ijcis.d.190711.001
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.2991/ijcis.d.190711.001
Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to \nsearch for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional \nDE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via \nincorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local \nSearch (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex \nlocal search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, \nan adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative \nduring the search process. A study of the important components of MFDEALS shows that there is a synergy between them. \nMFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the \nMDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE \nCEC’2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) \nnot only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. \nFurther, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework \nwith Alopex local search can significantly enhance the performance of L-SHADE
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2991/ijcis.d.190711.001
- https://www.atlantis-press.com/article/125913568.pdf
- OA Status
- gold
- Cited By
- 11
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2962878096
All OpenAlex metadata
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https://openalex.org/W2962878096Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2991/ijcis.d.190711.001Digital Object Identifier
- Title
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A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local SearchWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
-
Miguel León, Ning Xiong, Daniel Molina, Francisco HerreraList of authors in order
- Landing page
-
https://doi.org/10.2991/ijcis.d.190711.001Publisher landing page
- PDF URL
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https://www.atlantis-press.com/article/125913568.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.atlantis-press.com/article/125913568.pdfDirect OA link when available
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Memetic algorithm, Differential evolution, Computer science, Local search (optimization), Mathematical optimization, Algorithm, Artificial intelligence, MathematicsTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2, 2022: 3, 2021: 3, 2020: 2Per-year citation counts (last 5 years)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 52 |
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