This work was supported by the research projects: the National Natural Science Foundation of China under Grant Nos. 61502408, 61673331, 61772178 and 61403326, the postgraduate research and innovation Project of Hunan Province under Grant No. XDCX2019B057.
机构署名：
本校为其他机构
院系归属：
物理与电子工程学院
摘要：
Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm's convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (...
