Details of Research Outputs

Differential evolution (DE) relies mainly on its mutation mechanism to guide its search. Generally, the parents involved in mutation are randomly selected from the current population. Although such a mutation strategy is easy to use, it is inefficient for solving complex problems. Hence, how to utilize population information to further enhance the search ability of the mutation operator has become one of the most salient and active topics in DE. To address this issue, a new DE framework with the concept of index-based neighborhood, is proposed in this study. The proposed framework is named as neighborhood guided DE (NGDE). In NGDE, a neighborhood guided selection (NGS) is introduced to guide the mutation process by extracting the promising search directions with the neighborhood information. NGS includes four main operators: neighborhood construction, neighbors grouping, two-level neighbors ranking, and parents selection. With these four operators, NGS can utilize the topology and fitness information of population simultaneously. To evaluate the effectiveness of the proposed approach, NGS is applied to several original and advanced DE algorithms. Experimental results have shown that NGDE generally outperforms most of the corresponding DE algorithms on different kinds of optimization problems.