An improved self-adaptive memetic differential evolution (DE) algorithm

作者：Zhao Fuqing^*; Huo Mingming

来源：Journal of Information and Computational Science, 2012, 9(15): 4321-4328.

• Convergence speed
• Differential evolution algorithms
• Global convergence
• Local search
• Local search strategy
• Memetic
• Memetic algorithms
• Normal distribution model
• Premature convergence
• Self adaptive algorithms
• Self-adaptive

This paper proposes an improved self-adaptive memetic differential evolution algorithm(IMDE). In the aspects of population initialization and local search, the normal distribution model is introduced to improve the classic differential evolution algorithm in order to guarantees its higher optimizing efficiency and accuracy. The self-adaptive operators of mutation and crossover are introduced which not only improve the global convergence, but also guarantee the convergence speed of the algorithm. The simulation results show that IMDE has good global convergence and can avoid premature convergence effectively.

• 出版日期2012
• 单位西北工业大学