A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
Peng Zhang, Jinlong Li, Tengfei Li, Huanhuan Chen
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 MaOEA with DPPs (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs with different numbers of objectives. The experimental results demonstrate that MaOEADPPs is competitive.
Topics & Concepts
Determinantal point processConvergence (economics)Evolutionary algorithmMathematical optimizationKernel (algebra)MathematicsEvolutionary computationAlgorithmComputer sciencePopulationRandom matrixEigenvalues and eigenvectorsCombinatoricsQuantum mechanicsEconomicsPhysicsSociologyDemographyEconomic growthAdvanced Multi-Objective Optimization AlgorithmsProbabilistic and Robust Engineering DesignOptimization and Mathematical Programming