A Dynamic Knowledge-Guided Coevolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems
提出一种动态知识引导的协同进化算法，通过变量选择降维和双种群协同进化，解决大规模稀疏多目标优化问题，在基准和实际测试中优于五种现有算法。
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.