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Integrating Evolutionary Computation Components in Ant Colony Optimization

Integrating Evolutionary Computation Components in Ant Colony Optimization
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Author(s): Sergio Alonso (University of Granada, Spain), Oscar Cordon (University of Granada, Spain), Iñaki Fernández de Viana (Universidad de Huelva, Spain) and Francisco Herrera (University of Granada, Spain)
Copyright: 2005
Pages: 33
Source title: Recent Developments in Biologically Inspired Computing
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil) and Fernando J. Von Zuben (State University of Campinas, Brazil)
DOI: 10.4018/978-1-59140-312-8.ch007

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Abstract

This chapter introduces two different ways to integrate Evolutionary Computation Components in Ant Colony Optimization (ACO) Meta-heuristic. First of all, the ACO meta-heuristic is introduced and compared to Evolutionary Computation to notice their similarities and differences. Then two new models of ACO algorithms that include some Evolutionary Computation concepts (Best-Worst Ant System and exchange of memoristic information in parallel ACO algorithms) are presented with some empirical results that show improvements in the quality of the solutions when compared with more basic and classical approaches.

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