文献类型：期刊文献

英文题名：A Dynamic Knowledge-Guided Coevolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

作者：Li, Yingwei[1];Feng, Xiang[1];Yu, Huiqun[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：54

期号：11

起止页码：7054

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20244317266387);WOS:【SCI-EXPANDED(收录号:WOS:001336944400055)】；

语种：英文

外文关键词：Pareto optimization; Optimization; Heuristic algorithms; Dimensionality reduction; Neural networks; Input variables; Genetic operators; Cooperative coevolution; evolutionary algorithm (EA); large-scale multiobjective optimization; sparse Pareto optimal solutions

摘要：Large-scale sparse multiobjective optimization problems (SMOPs) exist widely in real-world applications, and solving them requires algorithms that can handle high-dimensional decision space while simultaneously discovering the sparse distribution of Pareto optimal solutions. However, it is difficult for most existing multiobjective evolutionary algorithms (MOEAs) to get satisfactory results. To address this problem, this article proposes a dynamic knowledge-guided coevolutionary algorithm, which employs a cooperative coevolutionary framework tailored for large-scale SMOPs. Specifically, variable selection is performed initially for the dimension reduction, and two populations are evolved in the original and reduced decision spaces, respectively. After offspring generation, variable replacement is performed to precisely identify the sparse distribution of Pareto optimal solutions. Furthermore, a dynamic score update mechanism is designed based on the discovered sparsity knowledge, which aims to adjust the direction of evolution dynamically. The superiority of the proposed algorithm is demonstrated by applying it to a variety of benchmark test instances and real-world test instances with the comparison of five other state-of-the-art MOEAs.