Adaptive Sampling Offspring Generation Strategy for Multi-objective Optimization

A covariance adaptive sampling offspring generation strategy (CASS) based on fuzzy clustering is proposed, and a multi-objective distribution estimation algorithm (MEDCA) based on this strategy is introduced. The GK-FCM clustering partitioning strategy is designed to build a Gaussian model for each individual, collectively approximating the manifold of the Pareto solution set and generating offspring through sampling. The introduction of an individual's survival generation adapts the individual's preference for exploration and exploitation. This is achieved by incorporating it as a scaling factor of the covariance matrix in the sampling model, in order to satisfy the individual's preferences for development and exploration in different evolutionary stages. This method significantly improves the performance of MEDCA in solving complex multi-objective optimization problems through covariance matrix adaptation sampling strategy and scaling factor adaptation strategy. The experimental results demonstrate the advantages of MEDCA in the application of offspring generation strategies during model sampling.