A K-Means Clustering-Based Hybrid Offspring Generation Mechanism in Evolutionary Multi-Objective Optimization

Abstract

Model-based recombination operators ignore individual quality information, while genetic-based differential evolution (DE) operators lack the extraction and use of global information. This makes it impossible for the single offspring generation method to always achieve excellent performance on various optimization problems. In order to solve the above problems, the K-means clustering-based hybrid offspring generation mechanism multi-objective evolutionary algorithm (KMDEA) is proposed. KMDEA performs K-means clustering on the population, and builds a multivariate Gaussian model based on the clustering results to discover the global information (the regularity property) of the population. For realizing the fusion of global and individual information, this paper designs a new hybrid offspring generation mechanism (KMD mechanism) to extract and use local individual information. Compared with a variety of mainstream multi-objective evolutionary algorithms (MOEAs), the results show that KMDEA has obvious advantages in solving multi-objective optimization problems (MOPs) with complex characteristics.