A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm
A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm
复制标题
一种基于自进化模糊系统在线预测的动态多目标进化算法
DOI：
10.1016/j.ins.2022.08.072
复制
发表时间：
2022-10
影响因子：
8.1
通讯作者：
仲兆满
中科院分区：
文献类型：
--
作者：
孙靖;甘兴家;巩敦卫;唐小珂;戴红伟;仲兆满
动态多目标优化问题在决策空间中的变化通常是非线性的。然而，以往的动态多目标进化算法通常采用线性预测模型来产生新环境下的初始种群，而一些非线性预测模型往往计算代价很高。因此，很难快速准确地响应非线性环境变化。提出了一种基于自进化模糊系统（SEFS）在线预测的动态多目标进化算法。在该算法中，基于分解的多目标进化算法（MOEA/D）作为静态优化。当环境发生变化时，首先将个体放入其相应权重向量的关联集合中。然后，基于关联集构造各变量的时间序列，建立SEFS在线预测模型。最后，设计了一种基于SEFS的环境响应策略，以快速生成新环境下具有高性能的初始种群。在20个基准函数上，将该算法与7种最先进的动态多目标进化算法进行了比较。实验结果表明，该算法能够快速准确地响应非线性环境变化，具有较强的竞争力。
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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影响因子：
14.3
作者：
Farina, M;Deb, K;Amato, P
通讯作者：
Amato, P
影响因子：
14.3
作者：
Goh, Chi-Keong;Tan, Kay Chen
通讯作者：
Tan, Kay Chen
DOI：
10.1016/j.swevo.2020.100829
发表时间：
2021
期刊：
Swarm Evol. Comput.
影响因子：
--
作者：
Sanyi Li;Shengxiang Yang;Yanfeng Wang;Weichao Yue;J. Qiao
通讯作者：
Sanyi Li;Shengxiang Yang;Yanfeng Wang;Weichao Yue;J. Qiao
DOI：
10.1109/cidue.2013.6595776
发表时间：
2013-04
期刊：
2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)
影响因子：
--
作者：
Mardé Helbig;A. Engelbrecht
通讯作者：
Mardé Helbig;A. Engelbrecht
DOI：
10.1007/978-3-540-70928-2_60
发表时间：
2007-03
期刊：
--
影响因子：
--
作者：
K. Deb;Udaya Bhaskara;Rao N;S. Karthik
通讯作者：
K. Deb;Udaya Bhaskara;Rao N;S. Karthik