원문정보
초록
영어
In order to improve the global searching ability of differential evolution algorithm in solving complex optimization problem, an improved differential evolution (SMDE) algorithm based on the self-adaptive method and multi-population is proposed in this paper. In the proposed SMDE algorithm, the population is divided into multi-populations in order to keep the diversity, then the self-adaptive method is used to control the parameters of differential evolution algorithm in order to balance the local search and global search ability. Finally, several complex benchmark functions are selected to validate the efficiency of the SMDE algorithm. The experiment results show that the proposed SMDE algorithm is better at the global convergence ability and the searching precision.
목차
1. Introduction
2. Differential Evolution
1. Generate the Initial Population
2. Mutation Operation
3. Crossover Operation
4. Selection Operation
3. An Improved Differential Evolution (SMDE) Algorithm
4. Experimental Results and Analysis
5. Conclusion
References