A Multimodel Prediction Method for Dynamic Multiobjective Evolutionary Optimization

被引:87
|
作者
Rong, Miao [1 ]
Gong, Dunwei [1 ,2 ]
Pedrycz, Witold [3 ]
Wang, Ling [4 ]
机构
[1] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China
[2] Qingdao Univ Sci & Technol, Sch Informat Sci & Technol, Qingdao 266061, Peoples R China
[3] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada
[4] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Sociology; Statistics; Optimization; Predictive models; Maintenance engineering; Mathematical model; Convergence; Dynamic multiobjective optimization; evolutionary algorithm (EA); multimodel prediction; particle swarm optimizer; type of the Pareto set (PS) change; ANT COLONY OPTIMIZATION; ALGORITHM; DIVERSITY; SEARCH; MEMORY;
D O I
10.1109/TEVC.2019.2925358
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A large number of prediction strategies are specific to a dynamic multiobjective optimization problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous DMOP with more than one type of the unknown PS change has been seldom investigated. We present a multimodel prediction approach (MMP) realized in the framework of evolutionary algorithms (EAs) to tackle the problem. In this paper, we first detect the type of the PS change, followed by the selection of an appropriate prediction model to provide an initial population for the subsequent evolution. To observe the influence of MMP on EAs, optimal solutions obtained by three classical dynamic multiobjective EAs with and without MMP are investigated. Furthermore, to investigate the performance of MMP, three state-of-the-art prediction strategies are compared on a large number of dynamic test instances under the same particle swarm optimizer. The experimental results demonstrate that the proposed approach outperforms its counterparts under comparison on most optimization problems.
引用
收藏
页码:290 / 304
页数:15
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