A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

DOI

10.2991/ijcis.d.190711.001How to use a DOI?

Keywords

Differential evolution; L-SHADE; Memetic algorithm; Alopex; Local search; Optimization

Abstract

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.

Copyright

© 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/).

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TY  - JOUR
AU  - Miguel Leon
AU  - Ning Xiong
AU  - Daniel Molina
AU  - Francisco Herrera
PY  - 2019
DA  - 2019/06/06
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  - 10.2991/ijcis.d.190711.001
ID  - Leon2019
ER  -