Affiliations: Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China | China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing, China | School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing, China
Note: [] Corresponding author. Xiaobing Yu, School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China. E-mail: yuxiaobing@nuist.edu.cn
Abstract: 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.
