Improving Selection Methods For Evolutionary Algorithms By Clustering

This study applies clustering in population selection to improve the efficiency of evolutionary algorithms. The main contributions include: (a) Proposes a novel selection framework that uses the number of clusters for a population as the measurement the population diversity. (b) Proposes clustering-ranking selection, an instance Of this framework, and discusses its mathematical principle by PD-SP equation. (c) Gives experiments over CLPSO (Comprehensive Learning Particle Swarm Optimization). Experiment result shows that the proposed selection method outperforms canonical exponential ranking on all the sixteen-benchmark functions for both 10-D and 30-D problem except a function for 30-D problem.