Large-scale Evolutionary Multiobjective Optimization: An Experimental Study

Evolutionary multiobjective optimization (EMO) has been a subject of intensive study in the past two decades, owing to its research challenges and practical values. With the progress and development of multiobjective evolutionary algorithms (MOEAs), recent research efforts have shifted to addressing large-scale EMO, which refers to applying evolutionary algorithms to solve multiobjective optimization problems with 100 or more decision variables. In this study, we delve into the design of eight large-scale MOEAs and evaluate their performance under different problem scales and computational resource. Based on the experimental results, we identify suitable algorithms in different scenarios. We also present observations and findings on the relationships between algorithm design concepts and performance.