Adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization-Reference-Cited by-同舟云学术

Adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization

Published:2022-01 Issue: Volume:583 Page:121-136
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Wang Zuling,Chen Ze,Wang Zidong,Wei Jing,Chen Xin,Li Qi,Zheng Yujun,Sheng Weiguo

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Royal Society

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference40 articles.

1. Feature subset selection using differential evolution and a wheel based search strategy;Al-Ani;Swarm Evol. Comput.,2013

2. Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction;Awad;Swarm Evol. Comput.,2018

3. Blackwell, T., Branke, J. Multi-swarm optimization in dynamic environments. In: Raidl G.R. et al. (eds) Applications of Evolutionary Computing. 2004. p. 489–500.

4. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems;Brest;IEEE Trans. Evol. Comput.,2006

5. Application of differential evolution algorithm for transient stability constrained optimal power flow;Cai;IEEE Trans. Power Syst.,2008

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