An improved differential evolution algorithm based on suboptimal solution mutation Online publication date: Tue, 21-Mar-2017
by Liang Chen; Chong Zhou; Xiangping Li; Guangming Dai
International Journal of Computing Science and Mathematics (IJCSM), Vol. 8, No. 1, 2017
Abstract: In order to improve the drawbacks of DE algorithm with DE/best/1 such as the rapid convergence speed and local optimum, this paper proposes an improved DE algorithm. Based on the DE/best/1 mutation operator, a new mutation operator is constructed. The best M individuals are summed as a new individual to replace the base individual of the DE/best/1. This is helpful to avoid falling into local optimum for the fast convergence. Simulation experiments demonstrate that the proposed algorithm outperforms some standard DE variants.
Online publication date: Tue, 21-Mar-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com