@Article{cmes.2021.017987,
AUTHOR = {Wei Wan, Gaige Wang, Junyu Dong},
TITLE = {Strengthened Initialization of Adaptive Cross-Generation Differential Evolution},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {130},
YEAR = {2022},
NUMBER = {3},
PAGES = {1495--1516},
URL = {http://www.techscience.com/CMES/v130n3/46087},
ISSN = {1526-1506},
ABSTRACT = {Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multiobjective 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 crossgeneration 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.},
DOI = {10.32604/cmes.2021.017987}
}