A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
- Miguel Leon1, *, Ning Xiong1, Daniel Molina2, Francisco Herrera21 IDT School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden2 DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain*Corresponding author. Email: email@example.com
- Corresponding Author
- Miguel Leon
- https://doi.org/10.2991/ijcis.d.190711.001How to use a DOI?
- Differential evolution, L-SHADE, Memetic algorithm, Alopex, Local search, Optimization
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.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Miguel Leon AU - Ning Xiong AU - Daniel Molina AU - Francisco Herrera PY - 2019 DA - 2019/07 TI - A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search JO - International Journal of Computational Intelligence Systems SP - 795 EP - 808 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190711.001 DO - https://doi.org/10.2991/ijcis.d.190711.001 ID - Leon2019 ER -