Scalarizing functions in decomposition-based multiobjective evolutionary algorithms

Decomposition-based multiobjective evolutionary algorithms have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions, 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 scalarizing functions and analyzing their effect in decomposition-based multiobjective evolutionary algorithms. Additionally, we come up with an efficient framework for decomposition-based multiobjective evolutionary algorithms based on the proposed scalarizing functions and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed scalarizing functions and algorithm.