Assignment of cells to switches in a cellular mobile network using a hybrid Hopfield network-genetic algorithm approach

Salcedo‐Sanz, Sancho; Yao, Xin

doi:10.1016/j.asoc.2007.01.002

Cited by 23 publications

(11 citation statements)

References 17 publications

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“…HYSA üzerindeki çalışmalar temel olarak donanımsal uygulama ve öğrenme algoritması geliştirme olarak sınıflandırılabilir. Genelleştirilmiş bir öğrenme algoritması elde etme çalışmaları günümüzde halen sürmektedir [3][4][5].…”

Section: Giriş şunclassified

Cellular Neural Network training by ant colony optimization algorithm

Ünal

Onat

Bal

2010

2010 IEEE 18th Signal Processing and Communications Applications Conference

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Özetçe Sahip olduğu paralel işleme yeteneği ile hücresel yapay sinir ağları (HYSA) görüntü işlemede önemli üstünlükler sunmaktadır. HYSA ile istenilen çıkış görüntüsünü elde edebilmek için şablon matrisleri katsayılarının ve eşik değerinin optimize edilmesi gerekmektedir. HYSA'nın dinamik yapısı, klasik ileri beslemeli yapay sinir ağlarında kullanılan öğrenme algoritmalarının kullanılmasına olanak sağlamamaktadır. HYSA'nın mimarisine uygun öğrenme algoritması geliştirme çalışmaları günümüzde de devam etmektedir. Bu çalışmada, çeşitli özellik çıkarımı uygulamaları için, karınca kolonisi optimizasyon (KKO) algoritması kullanılarak HYSA' nın eğitimi gerçekleştirilmiştir. Optimize edilen HYSA' nın, kenar, köşe, düşey ve yatay kenar tespiti uygulamalarında başarılı sonuçlar verdiği görülmüştür. Abstract Cellular Neural Networks (CNN) having parallel processing capabilities present important advantages in image processing applications. The coefficients of the template matrices and the threshold values of CNN should be optimized to obtain the desired output image. The learning algorithms designed for classical feed forward neural networks are not suitable for CNN due to its dynamic architecture. Researchers are still working on development of generalized learning algorithms for CNN. In this study, the CNN training is realized by ant colony optimization (ACO) technique. The results obtained by trained CNN show that ant colony based learning algorithm is very successful for image feature extraction problems such as edge, corner, vertical and horizontal edge detections. SIU2010 -IEEE 18.Sinyal isleme ve iletisim uygulamalari kurultayi -Diyarbakir 978-1-4244-9671-6/10/$26.00 ©2010 IEEE

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Section: Giriş şunclassified

Cellular Neural Network training by ant colony optimization algorithm

Ünal

Onat

Bal

2010

2010 IEEE 18th Signal Processing and Communications Applications Conference

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“…Instead, we develop an efficient algorithm based on GA, namely SR2CAB-GA, to solve the addressed problem. The reason is that GA is a robust search technology and recently has been validated to be suitable for the allocation problems [15,16], such as Lin and Yeh [7,8], Lee et al [17], Salcedo-Sanz et al [18], Salcedo-Sanz and Yao [19], Kaminer and Ben-Asher [20]. In the proposed algorithm, a two-class allocation is represented as a chromosome (a solution), and system reliability is evaluated in terms of MPs and state-space decomposition.…”

Section: Introductionmentioning

confidence: 98%

System reliability maximization for a computer network by finding the optimal two-class allocation subject to budget

Lin

Yeh

2015

Applied Soft Computing

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“…Let   be the Euclidean distance between BS i and MSC m, then the cabling cost   is defined as    ⋅  , where  is a positive multiplier. <Figure 2> and <Table 1> show the examples of the distance   between BS and MSC (Salcedo-Sanza and Yaob, 2008). We assumed that the handover cost is in proportional to the handover traffic.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…Several other heuristic techniques have been applied to the similar problem, such as Tabu Search (TS) (Pierre and Houéto, 2002), Simulated Annealing (Menson and Gupta, 2004), Genetic algorithm (Salcedo-Sanza and Yaob, 2008). Dianati et al (2003) suggested a Genetic based solution procedure for the problem.…”

Section: Introductionmentioning

confidence: 99%

Designing Cellular Mobile Network Using Lagrangian Based Heuristic

Hong¹,

Lee

2011

Journal of Korean Institute of Industrial Engineers

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Cellular network is comprised of several base stations which serve cellular shaped service area and each base station (BS) is connected to the mobile switching center (MSC). In this paper, the configuration modeling and algorithm of a cellular mobile network with the aim of minimizing the overall cost of operation (handover) and network installation cost (cabling cost and installing cost of mobile switching center) are considered. Handover and cabling cost is one of the key considerations in designing cellular telecommunication networks. For real-world applications, this configuration study covers in an integrated framework for two major decisions: locating MSC and assigning BS to MSC. The problem is expressed in an integer programming model and a heuristic algorithm based on Lagrangian relaxation is proposed to resolve the problem. Searching for the optimum solution through exact algorithm to this problem appears to be unrealistic considering the large scale nature and NP-Completeness of the problem. The suggested algorithm computes both the bound for the objective value of the problem and the feasible solution for the problem. A Lagrangian heuristics is developed to find the feasible solution. Numerical tests are performed for the effectiveness and efficiency of the proposed heuristic algorithm. Computational experiments show that the performance of the proposed heuristics is satisfactory in the quality of the generated solution.

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Assignment of cells to switches in a cellular mobile network using a hybrid Hopfield network-genetic algorithm approach

Cited by 23 publications

References 17 publications

Cellular Neural Network training by ant colony optimization algorithm

Cellular Neural Network training by ant colony optimization algorithm

System reliability maximization for a computer network by finding the optimal two-class allocation subject to budget

Designing Cellular Mobile Network Using Lagrangian Based Heuristic

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