Using the Cross-Entropy Method to Guide/Govern Mobile Agent’s Path Finding in Networks
Identifieur interne : 003036 ( Istex/Curation ); précédent : 003035; suivant : 003037Using the Cross-Entropy Method to Guide/Govern Mobile Agent’s Path Finding in Networks
Auteurs : Bjarne E. Helvik [Norvège] ; Otto Wittner [Norvège]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: The problem of finding paths in networks is general and many faceted with a wide range of engineering applications in communication networks. Finding the optimal path or combination of paths usually leads to NP-hard combinatorial optimization problems. A recent and promising method, the cross-entropy method proposed by Rubinstein, manages to produce optimal solutions to such problems in polynomial time. However this algorithm is centralized and batch oriented. In this paper we show how the cross-entropy method can be reformulated to govern the behaviour of multiple mobile agents which act independently and asynchronously of each other. The new algorithm is evaluate on a set of well known Travelling Salesman Problems. A simulator, based on the Network Simulator package, has been implemented which provide realistic simulation environments. Results show good performance and stable convergence towards near optimal solution of the problems tested.
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DOI: 10.1007/3-540-44651-6_24
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<front><div type="abstract" xml:lang="en">Abstract: The problem of finding paths in networks is general and many faceted with a wide range of engineering applications in communication networks. Finding the optimal path or combination of paths usually leads to NP-hard combinatorial optimization problems. A recent and promising method, the cross-entropy method proposed by Rubinstein, manages to produce optimal solutions to such problems in polynomial time. However this algorithm is centralized and batch oriented. In this paper we show how the cross-entropy method can be reformulated to govern the behaviour of multiple mobile agents which act independently and asynchronously of each other. The new algorithm is evaluate on a set of well known Travelling Salesman Problems. A simulator, based on the Network Simulator package, has been implemented which provide realistic simulation environments. Results show good performance and stable convergence towards near optimal solution of the problems tested.</div>
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