Terminal assignment in a communications network using genetic algorithms

Abuali, Faris N.; Schoenefeld, Dale A.; Wainwright, Roger L.

doi:10.1145/197530.197559

Cited by 35 publications

(30 citation statements)

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“…As it can be seen in table 6, for large instances (10)(11)(12), the standard deviations and the average fitnesses for GAS and QBEA are smaller. It means that these algorithms are more robust to solve large instances than GA, TS, HGA, GAMO, LSGA, HDE, HSS, DDE, HPBIL, HACO and BA.…”

Section: Resultsmentioning

confidence: 85%

“…Abuali et al [10] proposed a Greedy algorithm and a Hybrid Greedy-GA to solve TAP. Khuri and Chui [3] proposed a GA with a penalty function as alternative method to solve TAP and compared the results with the Greedy algorithm.…”

Section: Previous Workmentioning

confidence: 99%

“…The deterministic form is based on the Greedy algorithm proposed by Abuali et al [10]. The Greedy algorithm randomly assigns terminals to the closest feasible concentrators.…”

Section: Generation Of Solutionsmentioning

confidence: 99%

“…Several heuristics have been used to optimise TAP: Local Search (LS) methods [6][7][8][9], EAs [3][4][5][9][10][11][12][13][14][15][16][17][18], SI techniques [19,20], among others. Note however, that this paper presents the first attempt (to the authors' knowledge) to use evolutionary swarm based algorithms to optimise TAP.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Bernardino¹,

Bernardino²,

Sánchez‐Pérez³

et al. 2012

IJCIS

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In the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem -the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times.

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Section: Resultsmentioning

confidence: 85%

Section: Previous Workmentioning

confidence: 99%

“…The deterministic form is based on the Greedy algorithm proposed by Abuali et al [10]. The Greedy algorithm randomly assigns terminals to the closest feasible concentrators.…”

Section: Generation Of Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Bernardino¹,

Bernardino²,

Sánchez‐Pérez³

et al. 2012

IJCIS

View full text Add to dashboard Cite

show abstract

“…In this kind of situations, our hybrid metaheuristics does not perform better than greedy algorithms, existing in the literature. In order to show the differences in performance 376 SALCEDO-SANZ, XU AND YAO we have implemented the greedy approach proposed in [1], adapting it to the FSCRP. This algorithm is, in pseudo-code, as follows:…”

Section: Further Analysismentioning

confidence: 99%

Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

Salcedo‐Sanz

Yao

2005

Genet Program Evolvable Mach

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Abstract. In this paper we present three meta-heuristic approaches for FPGA segmented channel routing problems (FSCRPs) with a new cost function in which the cost of each assignment is not known in advance, and the cost of a solution only can be obtained from entire feasible assignments. Previous approaches to FSCPs cannot be applied to this kind of cost functions, and meta-heuristics are a good option to tackle the problem. We present two hybrid algorithms which use a Hopfield neural network to solve the problem's constraints, mixed with a Genetic Algorithm (GA) and a Simulated Annealing (SA). The third approach is a GA which manages the problem's constraints with a penalty function. We provide a complete analysis of the three metaheuristics, by tested them in several FSCRP instances, and comparing their performance and suitability to solve the FSCRP.

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Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks

Yao

Wang²,

Padmanabhan

et al.

Recent Advances in Memetic Algorithms

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Terminal assignment in a communications network using genetic algorithms

Cited by 35 publications

References 0 publications

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks

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