Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms
研究了基于分解的多目标进化算法中平衡多样性与收敛性的标量函数，提出了两种新标量函数及相应算法框架，实验验证了其有效性。
Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm.