Journal of System Simulation
Abstract
Abstract: The difficulties of designing a multi-strategy differential evolution (DE) algorithm are how to select the mutation strategies and allocate these strategies. A multi-strategy DE algorithm combined with the neighborhood search operator is proposed. The population is divided into three subpopulations according to the fitness values, and each subpopulation employs a different mutation strategy and parameter settings to complement the search ability, to balance the exploration and exploitation ability of the whole population. The subpopulation with the best fitness values employs the neighborhood search operator to exploit possible benefit information to guide the search. Extensive experiments are carried out on 34 test functions to compare with 12 different evolutionary algorithms, which include the 7 DE algorithms. The results show that the algorithm can perform better on most test functions.
Recommended Citation
Sun, Can; Zhou, Xinyu; and Wang, Mingwen
(2020)
"A Multi-strategy Differential Evolution Algorithm Combined with Neighborhood Search,"
Journal of System Simulation: Vol. 32:
Iss.
6, Article 10.
DOI: 10.16182/j.issn1004731x.joss.18-0787
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol32/iss6/10
First Page
1071
Revised Date
2019-07-15
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.18-0787
Last Page
1084
CLC
TP391.9
Recommended Citation
Sun Can, Zhou Xinyu, Wang Mingwen. A Multi-strategy Differential Evolution Algorithm Combined with Neighborhood Search[J]. Journal of System Simulation, 2020, 32(6): 1071-1084.
DOI
10.16182/j.issn1004731x.joss.18-0787
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