Dynamic multi-objective immune optimization algorithm based on prediction strategy

被引：0
|
作者：
Liu, Ruo-Chen [1 ]
Ma, Ya-Juan [1 ]
Zhang, Lang [1 ]
Shang, Rong-Hua [1 ]
机构：
[1] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an
来源：
Jisuanji Xuebao/Chinese Journal of Computers | 2015年 / 38卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Differential evolution; Dynamic multi-objective optimization; Forecasting model; Immune optimization algorithm;
D O I：
10.11897/SP.J.1016.2015.01544
中图分类号：
学科分类号：
摘要：
In this paper, a new dynamic multi-objective immune optimization algorithm based on prediction strategy is proposed for solving dynamic multi-objective optimization problems effectively. Firstly a similarity detection operation is used to detect the environment change. Then, a new forecasting model, which is established by the non-dominated antibodies in previous optimum locations, is adopted to generate the initial antibody population in order to improve the ability of responding to the environment change. Moreover, an improved differential evolution crossover operator based on two different selection strategies is introduced to speed the convergence of algorithm. The proposed algorithm is validated on several benchmark testing problems, the experimental result shows that the forecasting model based on the similarity detection operation can improve the tracking ability and the improved differential crossover operation can enhance the convergence. ©, 2015, Science Press. All right reserved.
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收藏
页码：1544 / 1560
页数：16
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