A framework based on generational and environmental response strategies for dynamic multi-objective optimization

Li，Qingya1; Liu，Xiangzhi2; Wang，Fuqiang2; Wang，Shuai3; Zhang，Peng2; Wu，Xiaoming2

2024-02-01

10.1016/j.asoc.2023.111114

Applied Soft Computing   影响因子和分区

1568-4946

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage cannot be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.

Center point Dynamic multi-objective optimization Genetic algorithm Novel dynamic framework Prediction

SCI ; EI

英语

第一

COMPUTER SCIENCE

2-s2.0-85181141423

Scopus

1.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Qilu University of Technology (Shandong Academy of Sciences),Shandong Computer Science Center (National Supercomputer Center in Jinan),Shandong Provincial Key Laboratory of Computer Networks,Shandong,China
3.Heze Branch,Qilu University of Technology (Shandong Academy of Sciences),Biological Engineering Technology Innovation Center of Shandong Province,Shandong,China

Li，Qingya,Liu，Xiangzhi,Wang，Fuqiang,et al. A framework based on generational and environmental response strategies for dynamic multi-objective optimization[J]. Applied Soft Computing,2024,152.

Li，Qingya,Liu，Xiangzhi,Wang，Fuqiang,Wang，Shuai,Zhang，Peng,&Wu，Xiaoming.(2024).A framework based on generational and environmental response strategies for dynamic multi-objective optimization.Applied Soft Computing,152.

Li，Qingya,et al."A framework based on generational and environmental response strategies for dynamic multi-objective optimization".Applied Soft Computing 152(2024).