Dynamic TSP Optimization Base on Elastic Adjustment

Song, Yingjie; Yong-yuan, Qin; Chen, Xianfu; Jin-chuan, You

doi:10.1109/icnc.2009.570

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In more practical objectives include, e.g., the traveling time which is subject to the ever changing road conditions and therefore is inherently dynamic. The Dynamic TSP was introduced for the first time by Psarafits [2] and various aspects of the DTSP are now the subject of intensive study [3][4][5].…”

Section: Traveling Salesman Problem Specificationmentioning

confidence: 99%

Using hyper populated ant colonies for solving the TSP

Siemiński

2016

Vietnam J Comput Sci

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The paper discusses the application of hyper populated ant colonies to the well-known traveling salesman problem (TSP). The ant colony optimization (ACO) approach offers reasonably good quality solutions for the TSP, but it suffers from its inherent non-determinism and as a consequence the processing time is unpredictable. The paper tries to mitigate the problem by a substantial increase in the number of used ants. This approach is called ant hyper population and it could be obtained by increasing the number of ants in a single colony assigning more than one colony to solve the same task or both. In all cases the level of nondeterminism decreases and thus the number iterations could be reduced. Parallel implementation of the ACO makes it possible to reduce drastically the processing time. The paper compares two ways of implementation of the parallelism using the sockets or the RMI-remote method invocation mechanisms. The paper concentrates on the classical static version of the TSP, but preliminary experiments indicate that such an approach could be even more useful for dynamic TPSs.

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Section: Traveling Salesman Problem Specificationmentioning

confidence: 99%

Using hyper populated ant colonies for solving the TSP

Siemiński

2016

Vietnam J Comput Sci

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“…In this sense, the adjective dynamic TSP is due to the presence of a dynamical system, as opposed to an explicit time-varying nature of the objective function or of the connections between the edges of a graph. While the latter have been extensively investigated, for instance in [1,2,6,7,12,13], the former has received less attention. This work can be seen as a multi-objective extension of the problem class tackled for instance in [3][4][5]17].…”

Section: Introductionmentioning

confidence: 99%

Solving multi-objective dynamic travelling salesman problems by relaxation

Ricciardi

Vasile

2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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This paper describes a method to solve Multi-objective Dynamic Travelling Salesman Problems. The problems are formulated as multi-objective hybrid optimal control problems, where the choice of the target destination for each phase is an integer variable. The resulting problem has thus a combinatorial nature in addition to being a multi-objective optimal control problem. The overall solution approach is based on a combination of the Multi Agent Collaborative Search, a population based memetic multi-objective optimisation algorithm, and the Direct Finite Elements Transcription, a direct method for optimal control problems. A relaxation approach is employed to transform the mixed integer problem into a purely continuous problem, and a set of smooth constraints is added in order to ensure that the relaxed variables of the final solution assume an integer value. A special set of smooth constraints is introduced in order to treat the mutually exclusive choices of the targets for each phase. The method is tested on two problems: the first is a motorised Travelling salesman problem described in the literature, the second is a space application where a satellite has to de-orbit multiple debris. For the first problem, the approach is generating better solutions than those reported in the literature.

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Parallel Implementations of the Ant Colony Optimization Metaheuristic

Siemiński

2016

Intelligent Information and Database Systems

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Dynamic TSP Optimization Base on Elastic Adjustment

Cited by 3 publications

References 5 publications

Using hyper populated ant colonies for solving the TSP

Using hyper populated ant colonies for solving the TSP

Solving multi-objective dynamic travelling salesman problems by relaxation

Parallel Implementations of the Ant Colony Optimization Metaheuristic

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