Shortest path routing algorithm using Hopfield neural network

Ahn, Chang Wook; Ramakrishna, R. S.; Kang, Chul-Hee; Choi, In Chan

doi:10.1049/el:20010800

Cited by 59 publications

(60 citation statements)

References 3 publications

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“…eLM method [7] : Regarding this criterion , as written above there are 8 major methods for introducing eLMs , in the current research , the selected method should enhanced with fun & enjoyment however such method is either Instructional Game method or Tutorial method. The selected case study is tutorial method mixed with some kind of Gaming method and enhanced with elements of fun & enjoyment.…”

Section: Instructional Computer Criteria [1]mentioning

confidence: 99%

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Enjoyable e-Learning Modules: Developing and Evaluating

Jabbar¹

2016

IJCA

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Section: Instructional Computer Criteria [1]mentioning

confidence: 99%

“…Scheduled time for answering or entering data [5] [7] it is better to allow learner learns with his own speed. It means realizes individualization of learning.…”

Section: General Criteriamentioning

confidence: 99%

Enjoyable e-Learning Modules: Developing and Evaluating

Jabbar¹

2016

IJCA

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“…The genetic algorithm, which was introduced by Holland [27] and was further described by Goldberg [28], is a stochastic optimization technique. The genetic algorithm is characterized by the following steps:…”

Section: Genetic Algorithmmentioning

confidence: 99%

A tabu search algorithm for routing optimization in mobile ad-hoc networks

Jang

2011

Telecommun Syst

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In this paper, we propose a routing optimization algorithm to efficiently determine an optimal path from a source to a destination in mobile ad-hoc networks. To determine an optimal path for the nodes is important for transmitting data between nodes in densely deployed networks. In order to efficiently transmit data to its destination, the appropriate routing algorithms must be implemented in mobile ad-hoc networks. The proposed algorithm is designed by using a tabu search mechanism that is a representative metaheuristic algorithm. The proposed tabu search algorithm carries out two neighborhood generating operations in order to determine an optimal path and minimize algorithm execution time. We compare the proposed tabu search algorithm with other meta-heuristic algorithms, which are the genetic algorithm and the simulated annealing, in terms of the routing cost and algorithm execution time. The comparison results show that the proposed tabu search algorithm outperforms the other algorithms and that it is suitable for adapting the routing optimization problem.

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“…With regard to difficulty of SPP; metaheuristics can also determine superior routes in a proper time (Mohemmed et al, 2008). Pervious researches also support the benefits of neural networks (NN) in handling SPP (Ahn et al, 2001). The genetic algorithm (GA) has been applied to realize different aspects of SPP (Ahn and Ramakrishna, 2002).…”

Section: Introductionmentioning

confidence: 99%

A Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

Heidari

Delavar

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical insta nces are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

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Shortest path routing algorithm using Hopfield neural network

Cited by 59 publications

References 3 publications

Enjoyable e-Learning Modules: Developing and Evaluating

Enjoyable e-Learning Modules: Developing and Evaluating

A tabu search algorithm for routing optimization in mobile ad-hoc networks

A Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

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