Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization

This file is the output data obtained when running the experiments from the paper below: Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” 2020 IEEE Congress on Evolutionary Computation Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.

本数据集为复现下述论文实验所得的输出数据：Ruan, G.、Minku, L.、Menzel, S.、Sendhoff, B.、Yao. 发表于2020年IEEE进化计算大会（IEEE Congress on Evolutionary Computation）的论文《动态多目标优化中知识迁移有效性的计算研究》（Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization）。迁移学习（Transfer Learning）已被广泛应用于各类优化及动态多目标优化（Dynamic Multi-objective Optimization, DMO）问题的求解，因其可从某一问题实例中迁移有效信息，辅助求解另一相关问题实例。本文旨在探究迁移学习在动态多目标优化中的应用效果。通过对迁移学习的计算耗时分析，我们发现迁移学习所引入的“内部”优化问题计算开销极高。为提升求解效率，我们针对多组动态双目标及三目标测试问题，对两种优化方案展开了计算层面的研究。实验结果表明，效率的大幅提升并未对迁移学习的整体性能造成显著退化。考虑到迁移学习本身的高计算成本，在动态多目标优化中使用迁移学习的原始目标可能会被抵消：换言之，优化过程中节省的计算时间，会被计算成本高昂的迁移学习所吞噬，最终整体计算效率的提升幅度远低于预期。为验证这一猜想，我们开展了对照实验，即以迁移学习的计算开销来优化随机生成的解。实验结果证实，在相同的总计算预算下，由随机解生成的最终解集的收敛性与多样性，均显著优于由迁移解生成的结果。