An Experimental Comparison between the Human Evolutionary Model and The Particle Swarm Optimizer Model

Abstract

The aim of this paper is to present an experimental comparison between the Human Evolutionary Model (HEM) and the Particle Swarm Optimizer (PSO) model. HEM is a novel intelligent computational method for solving search and optimization problems with single or multiple objectives, the evolution is conducted using expertise knowledge. The PSO model is a well known computational model based in swarm intelligence and it is inspired in the behavior of bird flocking. PSO has demonstrated its effectiveness optimizing standard test function, although it has fail optimizing composite test function, so likening both computational methods is an interesting issue. The Experiments shown are focused on optimizing single objective test functions.