Journal of System Simulation ›› 2018, Vol. 30 ›› Issue (5): 1690-1699.doi: 10.16182/j.issn1004731x.joss.201805009

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

Zhang Jinghua, Han Pu   

  1. Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University (Baoding), Baoding 71003, China
  • Received:2016-08-05 Revised:2017-06-30 Online:2018-05-08 Published:2019-01-03

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.

Key words: differential evolution algorithm (DE), co-optimization, multi-algorithm and multi-population, algorithm selection

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