Handling Time-Varying TSP Instances

Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based...

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Bibliographic Details
Published in2006 IEEE International Conference on Evolutionary Computation pp. 2830 - 2837
Main Authors de Franca, F.O., Gomes, L.C.T., de Castro, L.N., Von Zuben, F.J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Algorithm design and analysis
Ant colony optimization
Application software
Cities and towns
Computer networks
Cost function
Design optimization
Immune system
Proposals
Routing
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ISBN9780780394872
0780394879
ISSN1089-778X
DOI10.1109/CEC.2006.1688664

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Summary:Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immune-inspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature.
ISBN:9780780394872
0780394879
ISSN:1089-778X
DOI:10.1109/CEC.2006.1688664