进化算法
水准点(测量)
计算机科学
人口
进化计算
非线性系统
集合(抽象数据类型)
数学优化
变量(数学)
模糊逻辑
计算智能
算法
人工智能
数学
数学分析
物理
人口学
大地测量学
量子力学
社会学
程序设计语言
地理
作者
Jing Sun,Xingjia Gan,Dunwei Gong,Xiaoke Tang,Hongwei Dai,Zhaoman Zhong
标识
DOI:10.1016/j.ins.2022.08.072
摘要
The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness.
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