Adaptive Distributed Differential Evolution

• Published in: IEEE transactions on cybernetics Vol. 50; no. 11; pp. 4633 - 4647
• Main Authors: Zhan, Zhi-Hui, Wang, Zi-Jia, Jin, Hu, Zhang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-11-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive distributed differential evolution (ADDE); Algorithms; Crossovers; Cybernetics; differential evolution (DE); Evolutionary computation; evolutionary state estimation (ESE); Global optimization; historical successful experience (HSE); Indexes; master–slave multipopulation distributed; Mutation; Next generation networking; Optimization; Parameter sensitivity; Population; Populations; Sociology; State estimation; Statistics; Topology
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2019.2944873

Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor <inline-formula> <tex-math notation="LaTeX">{F} </tex-math></inline-formula> and crossover rate CR) and population parameter (population size <inline-formula> <tex-math notation="LaTeX">{N} </tex-math></inline-formula>), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants.