A distributed implementation of simulated annealing for the travelling salesman problem

Allwright, James; Carpenter, D.B.

doi:10.1016/0167-8191(89)90106-3

Cited by 61 publications

(29 citation statements)

References 4 publications

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“…Allwright and Carpenter [48] have solved the same problem on an array of transputers. These algorithms use parallelism to speed up the annealing process.…”

Section: Parallel Monte Carl0 Trialsmentioning

confidence: 93%

Model programs for computational science: A programming methodology for multicomputers

Hansen

1993

Concurrency: Pract. Exper.

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“…Allwright and Carpenter [48] have solved the same problem on an array of transputers. These algorithms use parallelism to speed up the annealing process.…”

Section: Parallel Monte Carl0 Trialsmentioning

confidence: 93%

Model programs for computational science: A programming methodology for multicomputers

Hansen

1993

Concurrency: Pract. Exper.

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“…Finally, the partitioning method connects each segment and obtains the final tour. There are two kinds of partitioning methods: one is Geometric Partitioning and the other is Tour-Based Partitioning [16][17][18]. Karp's Partitioning Heuristic (Karp) is proposed in [10].…”

Section: Partitioning Methodsmentioning

confidence: 99%

Solving Large-Scale TSP Using a Fast Wedging Insertion Partitioning Approach

Xiang

Chen

Gao

et al. 2015

Mathematical Problems in Engineering

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A new partitioning method, called Wedging Insertion, is proposed for solving large-scale symmetric Traveling Salesman Problem (TSP). The idea of our proposed algorithm is to cut a TSP tour into four segments by nodes' coordinate (not by rectangle, such as Strip, FRP, and Karp). Each node is located in one of their segments, which excludes four particular nodes, and each segment does not twist with other segments. After the partitioning process, this algorithm utilizes traditional construction method, that is, the insertion method, for each segment to improve the quality of tour, and then connects the starting node and the ending node of each segment to obtain the complete tour. In order to test the performance of our proposed algorithm, we conduct the experiments on various TSPLIB instances. The experimental results show that our proposed algorithm in this paper is more efficient for solving large-scale TSPs. Specifically, our approach is able to obviously reduce the time complexity for running the algorithm; meanwhile, it will lose only about 10% of the algorithm's performance.

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“…Simulated Annealing is inherently sequential and hence very slow for problems with large search spaces [41]. Several attempts have been made to speed up this process, such as development of parallel simulated annealing techniques [56] and special purpose computer architectures [57]. [42] indicated that the performance of SA algorithm depends on the cooling rate ∆T/L than on the individual values of ∆T and L, and that slow cooling schedules are generally more effective, and that the computational cost is in general improves with slower cooling rates.…”

Section: Sa Meta-heuristicmentioning

confidence: 99%