Multi-algorithm and Multi-population Co-optimization Differential Evolution Algorithm

Authors

Jinghua Zhang, Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University (Baoding), Baoding 71003, China;
Han Pu, Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University (Baoding), Baoding 71003, China;

Abstract

Abstract: Algorithm fusion or co-evolutionary with multi populations are the solutions for complex engineering application. A multi-algorithm and multi-population collaborative optimization algorithm is proposed by differential evolution (DE) algorithm, which pays emphasis on algorithm selection and combination. The algorithm designs a parameter-adaptive DE algorithm and selects three different DE algorithm variants which is complementary for each other and provides a multi-population co-optimization scheme according to four algorithms characters. Stimulation results show that the proposed algorithm could make four different algorithms remedy for each other, gets a better result, and raises the precision, reliability and suitability, which reduces algorithm selection difficulty in engineering application.

Recommended Citation

Zhang, Jinghua and Pu, Han (2019) "Multi-algorithm and Multi-population Co-optimization Differential Evolution Algorithm," Journal of System Simulation: Vol. 30: Iss. 5, Article 9.
DOI: 10.16182/j.issn1004731x.joss.201805009
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss5/9

First Page

1690

Revised Date

2017-06-30

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201805009

Last Page

1699

CLC

TP18;TP391.9

Recommended Citation

Zhang Jinghua, Han Pu. Multi-algorithm and Multi-population Co-optimization Differential Evolution Algorithm[J]. Journal of System Simulation, 2018, 30(5): 1690-1699.

DOI

10.16182/j.issn1004731x.joss.201805009