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A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, Granada, Spain..
Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, Granada, Spain..
2019 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 12, no 2, p. 795-808Article in journal (Refereed) Published
Abstract [en]

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 Memetic Differential Evolution with Alopex Local Search (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. 

Place, publisher, year, edition, pages
ATLANTIS PRESS , 2019. Vol. 12, no 2, p. 795-808
Keywords [en]
Differential evolution, L-SHADE, Memetic algorithm, Alopex, Local search, Optimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:mdh:diva-45267DOI: 10.2991/ijcis.d.190711.001ISI: 000483992100029OAI: oai:DiVA.org:mdh-45267DiVA, id: diva2:1352815
Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2019-09-19Bibliographically approved

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Leon, MiguelXiong, Ning

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