@misc{10481/59477,
year = {2019},
url = {http://hdl.handle.net/10481/59477},
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},
organization = {This research was supported by grants from both Swedish Research Council
(project number 2016-05431) and Spanish Ministry of Science TIN2016-
8113-R.},
publisher = {Atlantis Press},
keywords = {Differential evolution},
keywords = {Memetic algorithm},
keywords = {Local search},
keywords = {Optimization},
title = {A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search},
doi = {10.2991/ijcis.d.190711.001},
author = {León, Miguel and Xiong, Ning and Molina Cabrera, Daniel and Herrera Viedma, Enrique},
}