Paper Info
Title
An individual-dependent differential evolution with dual information guidance
Abstract
To enhance the effectiveness of differential evolution (DE) algorithm, in recent years, a number of DE variants have been proposed by employing the idea of multiple strategies. Although these DE variants have been shown competitive performance, there still exists a problem for them that the strategy selection mechanism mainly relies on the historical search experience. Unfortunately, the historical search experience may be suitable for the problems with a flat fitness landscape, while not for the problems with a rugged fitness landscape. To alleviate the issue, in this work, an individual-dependent DE variant, called IPDE, is proposed by using the dual information guidance, including the fitness information and spatial information. In the IPDE, the idea of multiple strategies is used as well, but its most salient feature lies in that the strategy selection mechanism is based on the individual role rather than the historical search experience. To better identify the individual role, the fitness information and spatial information are used simultaneously, which can roughly estimate the evolutionary statuses of different individuals in the fitness landscape. To validate the effectiveness of IPDE, 52 benchmark functions are used in the experiments, and four well-established evolutionary algorithms are included in the performance comparison. The final results show that IPDE can achieve promising performance.
Year
DOI
Venue
2021
10.1109/ICTAI52525.2021.00185
2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021)
Keywords
DocType
ISSN
differential evolution, multiple strategies, individual role, fitness information, spatial information
Conference
1082-3409
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
xinyu zhou129223.26
Yanlin Wu200.68
Hu Peng34613.63
Shuixiu Wu400.34
Mingwen Wang531538.28