Evolutionary Large-Scale Multiobjective Optimization: Benchmarks and Algorithms
研究了进化大规模多目标优化的现有算法和基准测试，发现两者均需改进，因此提出了新的测试套件和优化器框架，新基准包含更真实特征，新优化器采用变量分组学习策略，实验验证了其优势。
Evolutionary large-scale multiobjective optimization (ELMO) has received increasing attention in recent years. This study has compared various existing optimizers for ELMO on different benchmarks, revealing that both benchmarks and algorithms for ELMO still need significant improvement. Thus, a new test suite and a new optimizer framework are proposed to further promote the research of ELMO. More realistic features are considered in the new benchmarks, such as mixed formulation of objective functions, mixed linkages in variables, and imbalanced contributions of variables to the objectives, which are challenging to the existing optimizers. To better tackle these benchmarks, a variable group-based learning strategy is embedded into the new optimizer framework for ELMO, which significantly improves the quality of reproduction in large-scale search space. The experimental results validate that the designed benchmarks can comprehensively evaluate the performance of existing optimizers for ELMO and the proposed optimizer shows distinct advantages in tackling these benchmarks.