A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

TL;DR

This paper introduces MaOEADPPs, a new many-objective evolutionary algorithm utilizing Determinantal Point Processes to effectively balance convergence and diversity in high-dimensional objective spaces, showing competitive results.

Contribution

The paper presents MaOEADPPs, a novel algorithm that integrates DPPs to improve diversity and convergence in many-objective optimization.

Findings

MaOEADPPs outperforms several state-of-the-art algorithms on various MaOPs.

The algorithm effectively balances diversity and convergence in high-dimensional spaces.

Experimental results demonstrate its competitiveness across different objectives.

Abstract

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.