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- Title
Strengthened Initialization of Adaptive Cross-Generation Differential Evolution.
- Authors
Wei Wan; Gaige Wang; Junyu Dong
- Abstract
Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multi- objective problems with remarkable performance compared to other evolutionary algorithms (EAs). However, its convergence and diversity are not satisfactory compared with the latest algorithms. In order to adapt to the current environment, ACGDE requires improvements in many aspects, such as its initialization and mutant operator. In this paper, an enhanced version is proposed, namely SIACGDE. It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor. These improvements make the direction of cross- generation mutation more clearly and the ability of searching more efficiently. The experiments show that the new algorithm has better diversity and improves convergence to a certain extent. At the same time, SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.
- Subjects
DIFFERENTIAL evolution; EVOLUTIONARY algorithms; ALGORITHMS
- Publication
CMES-Computer Modeling in Engineering & Sciences, 2022, Vol 130, Issue 3, p1495
- ISSN
1526-1492
- Publication type
Article
- DOI
10.32604/cmes.2021.017987