|Table of Contents|

Differential evolution algorithm based on population classification(PDF)

《纺织高校基础科学学报》[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2017年02期
Page:
272-278
Research Field:
Publishing date:

Info

Title:
Differential evolution algorithm based on population classification
Author(s):
YAN Xueqing GAO Xingbao
 School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China
Keywords:
 differential evolution stochastic method population classification mutation strategy
PACS:
TP 18
DOI:
10.13338/j.issn.1006-8341.2017.02.019
Abstract:
To prevent differential evolution algorithm from falling into local optimum and reducing the convergence rate, a differential evolution algorithm based on population classification is proposed. The proposed algorithm firstly divides the whole population into three sub-populations(superior, general and inferior sub-populations)by means of chosing three individuals randomly from the population and comparing with target individuals according to their fitness values. Then, three mutation operators with different characteristics are assigned for each sub-population above according to their special individual information, and control parameters among each mutation operator are suitably adjusted. The proposed algorithm could not only enhance the robustness, but also balance effectively the exploration and exploitation abilities by making full use of the information of individuals. Lastly, the effectiveness of this algorithm is shown by numerical experiments.

References:

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Last Update: 2017-07-22