Hybridizing cellular GAs with active components of bio-inspired algorithms

Abstract

Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO) are two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems. In this work we present two hybrid algorithms based on a cGA with the insertion of components from PSO. We aim to achieve significant numerical improvements in the results obtained by a cGA in combinatorial optimization problems. We here analyze the performance of our hybrids using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg.