改进种群多样性的双变异差分进化算法

doi:10.15960/j.cnki.issn.1007-6093.2017.01.005

Operations Research Transactions

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Differential evolution algorithm with double mutation strategies for improving population diversity

LI Rongyu¹ CHEN Qingqian^1,* CHEN Feier¹

1. College of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China

Received:2016-02-01 Online:2017-03-15 Published:2017-03-15

Abstract

Abstract:

Differential Evolution (DE) is an efficient population-based heuristic stochastic search technique. It is robust for solving continuous optimization problems. However, the discrepancy of population diversity and convergence rate exists in traditional Differential Evolution. In this paper, differential evolution algorithm based on double mutation strategies for improving population diversity (DADE}) was proposed. This algorithm presents a BFS-best mechanism to improve ``current-to-best'', which cooperates with DE/rand/1 to ensure population diversity. Meanwhile, the control parameters of individuals are updated automatically based on ranking. Finally, several benchmark functions in CEC2013 are used to test the proposed algorithm. The simulation results show that DADE can effectively improve population diversity, achieve better global searching ability and a higher convergence rate.

Key words: differential evolution, population diversity, double mutation strategy, ranking

LI Rongyu, CHEN Qingqian, CHEN Feier. Differential evolution algorithm with double mutation strategies for improving population diversity[J]. Operations Research Transactions, doi: 10.15960/j.cnki.issn.1007-6093.2017.01.005.

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Differential evolution algorithm with double mutation strategies for improving population diversity

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