A genetic algorithm for the maximum edge-disjoint paths problem

Hsu, Chia Chun; Cho, Hsun-Jung

doi:10.1016/j.neucom.2012.10.046

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…Therefore, we separate our final experimentation into two different comparisons. In the first, we compare the two‐stage method proposed in this paper (parametrized with the configuration described above) with the extended artificial ant colony (E‐ACO), the multistart simple greedy (MSGA) (both described in Blesa and Blum, ), and the GA proposed in (Hsu and Cho, ). For the sake of completeness, we also include the ILP and EA executed in isolation for a similar time than the ILP + EA.…”

Section: Resultsmentioning

confidence: 99%

“…*We have not included results of GA over mesh25x25 subset since the authors did not provide them in Hsu and Cho ().…”

Section: Resultsmentioning

confidence: 99%

“…The average number of disjoint paths and the average computing time are directly copied from Blesa and Blum () and Hsu and Cho (). The computer used to execute MSGA and E‐ACO is an Intel(R) Pentium(R) 4 processor at 3.06 GHz and 900 MB of memory running a Linux operating system with 1.98 processor load, implemented in C++, and compiled using GCC 2.95.2 with the ‐O3 option.…”

Section: Resultsmentioning

confidence: 99%

“…Our experiments were conducted on a Pentium(R) Dual‐Core CPU at 3.20 GHz, and 4 GB of RAM memory. Our experimental platform can be considered equivalent to the one used by Hsu and Cho (). On the other hand, the experimental platform used by Blesa and Blum () is considerably older.…”

Section: Resultsmentioning

confidence: 99%

“…Hsu and Cho () presented a genetic algorithm (GA) for solving the EDP problem on general graphs. Specifically, each individual in the population is encoded as a set of

| T |

paths and each path is represented by a string of real values with length

| V |

in the interval [0,1].…”

Section: Related Workmentioning

confidence: 99%

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Solving the edge‐disjoint paths problem using a two‐stage method

Martín

Sánchez

Beltrán-Royo

et al. 2018

Int Trans Operational Res

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There exists a wide variety of network problems where several connection requests occur simultaneously. In general, each request is attended by finding a route in the network, where the origin and destination of such a route are those hosts that wish to establish a connection for information exchange. As is well documented in the related literature, the exchange of information through disjoint routes increases the effective bandwidth, velocity, and the probability of receiving the corresponding information. The definition of disjoint paths may refer to nodes, edges, or both. One of the most studied variants is the one where disjointness implies not to share edges. This optimization problem is usually known as the maximum edge‐disjoint paths problem. This scriptNP‐hard optimization problem has applications in real‐time communications, very large scale integration design, scheduling, bin packing, or load balancing. The proposed approach hybridizes an integer linear programming formulation of the problem with an evolutionary algorithm. Empirical results using 174 previously reported instances show that the proposed procedure compares favorably to previous metaheuristics for this problem. We confirm the significance of the results by conducting nonparametric statistical tests.

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Section: Resultsmentioning

confidence: 99%

“…*We have not included results of GA over mesh25x25 subset since the authors did not provide them in Hsu and Cho ().…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Hsu and Cho () presented a genetic algorithm (GA) for solving the EDP problem on general graphs. Specifically, each individual in the population is encoded as a set of