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Knowledge Transfer With Mixture Model in Dynamic Multiobjective Optimization

计算机科学 数学优化 人工智能 数学
作者
Juan Zou,Zhanglu Hou,Shouyong Jiang,Shengxiang Yang,Gan Ruan,Yizhang Xia,Yuan Liu
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:29 (5): 1517-1530 被引量:11
标识
DOI:10.1109/tevc.2025.3566481
摘要

Most existing dynamic multi-objective evolutionary algorithms (DMOEAs) have been designed to handle dynamic multi-objective optimization problems (DMOPs) with regular environmental changes. However, they often overlook scenarios where environmental changes are irregular and less predictable. Recently, knowledge transfer has been proposed as a novel paradigm for solving DMOPs. Despite this, most transfer strategies only consider transferring knowledge obtained from the previous environment while ignoring significant differences that may exist between adjacent environments due to irregular changes. To address these issues, this paper proposes a novel knowledge transfer strategy based on a Gaussian mixture model (denoted as KTMM) for solving DMOPs with irregular changes. In particular, an adaptive Gaussian mixture model is designed to capture the knowledge of historical environments, which is then transferred to generate an initial population for the new environment. Additionally, a new method for controlling irregular changes is introduced into widely-used benchmarks to form the DMOP benchmark with irregular changes. Our proposed KTMM is compared with six state-of-the-art DMOEAs on several benchmark problems with irregular changes. Experimental results demonstrate the superiority of our proposed method in most test instances and in a real-world problem.
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