A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
This work provides an incremental improvement for researchers and practitioners working on many-objective optimization problems, aiming to enhance the balance between convergence and diversity in evolutionary algorithms.
This paper addresses the challenge of balancing convergence and diversity in Many-Objective Evolutionary Algorithms (MaOEAs) for high-dimensional objective spaces. The proposed MaOEADPPs algorithm, which incorporates Determinantal Point Processes, shows competitive performance against state-of-the-art algorithms across various Many-Objective Optimization Problems.
To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel Matrix and probability model called Determinantal Point Processes (DPPs). Our Many-Objective Evolutionary Algorithm with Determinantal Point Processes (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs \textcolor{blue}{with different numbers of objectives}. The experimental results demonstrate that MaOEADPPs is competitive.