Self-adaptive dual-strategy differential evolution algorithm


Autoři: Meijun Duan aff001;  Hongyu Yang aff001;  Shangping Wang aff003;  Yu Liu aff002
Působiště autorů: National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China aff001;  College of Computer Science, Sichuan University, Chengdu, China aff002;  Science and Technology on Electronic Information Control Laboratory, Chengdu, China aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222706

Souhrn

Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the “DE/best/2” mutation operator with better global exploration ability and “DE/rand/2” mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems’ dimension.

Klíčová slova:

Algorithms – Convergent evolution – Evolutionary algorithms – Mutation detection – Optimization – Species diversity – Evolutionary immunology – Bacterial evolution


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PLOS One


2019 Číslo 10

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