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Integrating Evolutionary Computation Components in Ant Colony Optimization

Integrating Evolutionary Computation Components in Ant Colony Optimization

Sergio Alonso, Oscar Cordon, Iñaki Fernández de Viana, Francisco Herrera
Copyright: © 2005 |Pages: 33
ISBN13: 9781591403128|ISBN10: 159140312X|ISBN13 Softcover: 9781591403135|EISBN13: 9781591403142
DOI: 10.4018/978-1-59140-312-8.ch007
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MLA

Alonso, Sergio, et al. "Integrating Evolutionary Computation Components in Ant Colony Optimization." Recent Developments in Biologically Inspired Computing, edited by Leandro Nunes de Castro and Fernando J. Von Zuben, IGI Global, 2005, pp. 148-180. https://doi.org/10.4018/978-1-59140-312-8.ch007

APA

Alonso, S., Cordon, O., de Viana, I. F., & Herrera, F. (2005). Integrating Evolutionary Computation Components in Ant Colony Optimization. In L. Nunes de Castro & F. Von Zuben (Eds.), Recent Developments in Biologically Inspired Computing (pp. 148-180). IGI Global. https://doi.org/10.4018/978-1-59140-312-8.ch007

Chicago

Alonso, Sergio, et al. "Integrating Evolutionary Computation Components in Ant Colony Optimization." In Recent Developments in Biologically Inspired Computing, edited by Leandro Nunes de Castro and Fernando J. Von Zuben, 148-180. Hershey, PA: IGI Global, 2005. https://doi.org/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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