標題: | Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems |
作者: | Han, Ming-Feng Liao, Shih-Hui Chang, Jyh-Yeong Lin, Chin-Teng 電控工程研究所 Institute of Electrical and Control Engineering |
關鍵字: | Evolutionary algorithm (EA);Differential evolution (DE);Adaptive strategy;Optimization |
公開日期: | 1-七月-2013 |
摘要: | This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all individuals in the population are grouped into a superior group and an inferior group based on their fitness values. The two groups perform different mutation operations. The local mutation model is applied to individuals with better fitness values, i.e., in the superior group, to search for better solutions near the current best position. The global mutation model is applied to the inferior group, which is composed of individuals with lower fitness values, to search for potential solutions. Subsequently, the GDE algorithm employs crossover and selection operations to produce offspring for the next generation. In this paper, an adaptive tuning strategy based on the well-known 1/5th rule is used to dynamically reassign the group size. It is thus helpful to trade off between the exploration ability and the exploitation ability. To validate the performance of the GDE algorithm, 13 numerical benchmark functions are tested. The simulation results indicate that the approach is effective and efficient. |
URI: | http://dx.doi.org/10.1007/s10489-012-0393-5 http://hdl.handle.net/11536/22270 |
ISSN: | 0924-669X |
DOI: | 10.1007/s10489-012-0393-5 |
期刊: | APPLIED INTELLIGENCE |
Volume: | 39 |
Issue: | 1 |
起始頁: | 41 |
結束頁: | 56 |
顯示於類別: | 期刊論文 |