Year: 2014
Type: conference-paper
Abstract: t-Differential evolution algorithm in solving complex function optimization problems, the problems of convergence rate and precision is not high.At the same time, there is a big difference in the performance of evolutionary algorithms for solving the different types of optimization problems.To solve above two problems, this paper proposes a dynamic multimodal differential evolution algorithm.Firstly, the dynamically population is used to improve the exploration ability of algorithms; In addition, the algorithm uses Four different types of mutation operator to Produce among individuals, choose the best among individuals to enter the next iteration , improved the algorithms's performance of solving different types of optimization problems.Through a variety of BenchMark functions to the algorithm simulation experiment, and comparing and several other classical differential evolution algorithm, show that this algorithm has better optimization performance.
Authors: XiaoGang Dong, Qing Xie, Lin Ke
Institution: Jiujiang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1992721470