Counterintuitive Experimental Results in Evolutionary Large-Scale Multiobjective Optimization
该文发现，增加决策变量数有时反而能改善多目标优化结果，且传统算法可能优于专门的大规模算法。通过分析反直觉现象，为如何选择测试问题与算法提供了建议。
Recently, large-scale multiobjective optimization has received increasing attention from the evolutionary multiobjective optimization (EMO) community. This has led to the emergence of a specialized research area called evolutionary large-scale multiobjective optimization (ELMO). In general, it is believed that multiobjective optimization problems become more difficult as the number of decision variables increases. However, the following two counterintuitive observations are obtained from careful examinations of recent ELMO studies. One is that experimental results on some large-scale multiobjective test problems were improved by increasing the number of decision variables. The other is that better results were obtained for some other large-scale multiobjective test problems by conventional EMO algorithms (EMOAs) than state-of-the-art ELMO algorithms (ELMOAs). These observations suggest that ELMOAs have not always been evaluated on appropriate test problems. Moreover, their performance is not always better than the performance of conventional EMOAs. In this letter, we first re-examine the performance of ELMOAs and conventional EMOAs on a wide variety of scalable multiobjective test problems. Then, counterintuitive experimental results are analyzed using the anytime performance evaluation scheme and distributions of randomly generated initial solutions. Based on the analysis, suggestions on how to handle large-scale multiobjective test problems with counterintuitive results are proposed.