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A Deterministic Metaheuristic Approach Using “Logistic Ants” for Combinatorial Optimization

Identifieur interne : 003198 ( Main/Merge ); précédent : 003197; suivant : 003199

A Deterministic Metaheuristic Approach Using “Logistic Ants” for Combinatorial Optimization

Auteurs : Rodolphe Charrier [France] ; Christine Bourjot [France] ; François Charpillet [France]

Source :

RBID : ISTEX:AFD62D3985EABEE27F4095A7A9306D405A64DFE7

English descriptors

Abstract

Abstract: Ant algorithms are usually derived from a stochastic modeling based on some specific probability laws. We consider in this paper a full deterministic model of “logistic ants” which uses chaotic maps to govern the behavior of the artificial ants. We illustrate and test this approach on a TSP instance, and compare the results with the original Ant System algorithm. This change of paradigm —deterministic versus stochastic— implies a novel view of the internal mechanisms involved during the searching and optimizing process of ants.

Url:
DOI: 10.1007/978-3-642-15461-4_30

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ISTEX:AFD62D3985EABEE27F4095A7A9306D405A64DFE7

Le document en format XML

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<div type="abstract" xml:lang="en">Ant algorithms are usually derived from a stochastic modeling based on some specific probability laws. We consider in this paper a full deterministic model of “logistic ants” which uses chaotic maps to govern the behavior of the artificial ants. We illustrate and test this approach on a TSP instance, and compare the results with the original Ant System algorithm. This change of paradigm —deterministic versus stochastic— implies a novel view of the internal mechanisms involved during the searching and optimizing process of ants.</div>
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<div type="abstract" xml:lang="en">Abstract: Ant algorithms are usually derived from a stochastic modeling based on some specific probability laws. We consider in this paper a full deterministic model of “logistic ants” which uses chaotic maps to govern the behavior of the artificial ants. We illustrate and test this approach on a TSP instance, and compare the results with the original Ant System algorithm. This change of paradigm —deterministic versus stochastic— implies a novel view of the internal mechanisms involved during the searching and optimizing process of ants.</div>
</front>
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