期刊
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 28, 期 3, 页码 1047-1060出版社
IOS PRESS
DOI: 10.3233/IFS-141388
关键词
Evolutionary algorithm; global optimization; differential evolution; DE/best/2; particle swarm optimization
资金
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
- Social Science Foundation of Chinese Ministry of Education [12YJC630271]
- Natural Science Fund for Colleges and Universities in Jiangsu Province [13KJB120008]
- China Natural Science Foundation [71273139, 71401078]
- China Institute of Manufacturing Development [SK20130090-15]
Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The success of DE in solving a specific problem crucially depends on appropriately choosing generation strategies and control parameter values. A novel mutation DE (MDE) is proposed to improve generation strategy DE/best/2. Adaptive mutation is conducted to current population when the population clusters around local optima. Control parameters are adapted based on constants. The performance of MDE is extensively evaluated on eighteen benchmark functions and compares favorably with the several DE variants.
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