Hybridizing Cellular GAs with Active Components of Bio-inspired Algorithms

Part of the Studies in Computational Intelligence book series (SCI, volume 434)

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.

Keywords

Genetic Algorithm Particle Swarm Optimization Hybrid Algorithm Parallel Genetic Algorithm Particle Swarm Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MálagaMálagaSpain
  2. 2.Emerging Technologies LaboratoryUniversidad Nacional de la Patagonia AustralRío GallegosArgentine

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