.png){kind=icon}
.png){kind=icon}
Return
Differential evolution with improved individual-based parameter setting and selection strategy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2017.03.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a novel differential evolution (DE) algorithm is proposed to improve the search efficiency of DE by employing the information of individuals to adaptively set the parameters of DE and update population. Firstly, a combined mutation strategy is developed by using two mixed mutation strategies with a prescribed probability. Secondly, the fitness values of original and guiding individuals are used to guide the parameter setting. Finally, a diversity-based selection strategy is designed by assembling greedy selection strategy and defining a new weighted fitness value based on the fitness values and positions of target and trial individuals. The proposed algorithm compares with eight existing algorithms on CEC 2005 and 2014 contest test instances, and is applied to solve the Spread Spectrum Radar Polly Code Design. Experimental results show that the proposed algorithm is very competitive. (C) 2017 Published by Elsevier B.V.
Keywords:
Differential evolution
Global optimization
Combined mutation strategy
Parameters setting
Selection strategy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W