Multifactorial Evolutionary Algorithm Based on Improved Dynamical Decomposition for Many-Objective Optimization Problems

Abstract

In multiobjective optimization, it is generally known that the boom in computational complexity and search spaces came with a rise in the number of objectives, and this leads to a decrease in selection pressure and the deterioration of the evolutionary process. It follows then that the many-objective optimization problem (MaOP) has become one of the most challenging topics in the field of intelligent optimization. Recently, the multifactorial evolutionary algorithm (MFEA) and its variations, which have shown excellent performance in knowledge transfer across related problems, may provide a new and effective way for solving MaOPs. In this article, a novel MFEA based on improved dynamical decomposition (MFEA/IDD), which integrates the advantages of multitasking optimization and decomposition-based evolutionary algorithms, is proposed. Specifically, in the improved dynamical decomposition strategy (IDD) method, the bi-pivot strategy is designed to provide a good mechanism for balancing between convergence and diversity instead of the single-pivot strategy. Furthermore, a novel MFEA-based approach embedding the IDD strategy is developed to reduce the total running time for solving multiple MaOPs simultaneously. Compared with seven state-of-the-art algorithms, the efficacy of our proposed method is validated experimentally on the benchmarks WFG, DTLZ, and MAF with three to ten objectives, along with a series of real-world cases. The results reveal that the MFEA/IDD is well placed in balancing convergence and diversity while reducing the total number of function evaluations for solving MaOPs.