Dynamic Multiobjective Evolutionary Optimization via Knowledge Transfer and Maintenance
提出一种新的动态多目标进化算法KTM-DMOEA，通过知识迁移预测和知识维护采样两种策略，从历史环境中挖掘有用知识，缓解负迁移并提升优化效率，在基准和实际问题上表现优于现有算法。
This article suggests a new dynamic multiobjective evolutionary algorithm (DMOEA) with Knowledge Transfer and Maintenance, called KTM-DMOEA, which aims to alleviate the negative transfer and enhance the optimization efficiency. Two strategies, i.e., knowledge transfer prediction (KTP) and knowledge maintenance sampling (KMS), are proposed to excavate useful knowledge from historical environments. Particularly, KTP is a discriminative predictor designed to reduce the feature and distribution divergences across distinct environments, which classifies high-quality solutions from a large number of randomly generated solutions in new environment. Moreover, KMS is a generative predictor by modeling the distribution of elitist solutions in last environment, which can sample superior solutions in new environment according to the dynamic change trends. In this way, the advantages of KTP and KMS strategies are combined to produce a superior initial population in new environment, which help to alleviate the negative transfer and resultantly enhance the overall performance of KTM-DMOEA. When compared to several recently reported DMOEAs, the experimental results validate the advantages of KTM-DMOEA in tackling most cases of benchmark and real-world problems.