When and How to Transfer Knowledge in Dynamic Multi-objective Optimization

This file is the output data obtained when running the experiments of the paper below: Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.本文件为运行下述论文实验时所得的输出数据：Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., 《动态多目标优化中的知识迁移时机与方式》（When and How to Transfer Knowledge in Dynamic Multi-objective Optimization），2019 IEEE计算智能系列研讨会（IEEE Symposium Series on Computational Intelligence, SSCI），中国厦门，2019年，第2034-2041页。迁移学习（Transfer Learning）已被应用于求解各类优化问题与动态多目标优化（Dynamic Multi-objective Optimization）问题，因其可从某一问题中迁移有效信息，以辅助解决另一相关问题。本文旨在探究迁移学习在动态多目标优化中生效或失效的时机与方式。通过对多个动态双目标及三目标基准测试问题开展计算分析，本文证实：当帕累托最优解集（Pareto Optimal Solution Set）固定，且环境变化幅度较小时，迁移学习会失效。同时本文还发现，现有基于迁移学习的方法所采用的高斯核函数（Gaussian Kernel Function）并非始终适用。因此，在迁移学习失效的问题场景中应避免使用迁移学习，而当高斯核函数不再适配时，需选用其他核函数。本文提出了可应用于此类场景的新型策略与核函数。实验研究表明，在多个动态双目标及三目标测试问题上，本文所提技术的性能优于当前顶尖方法。