Displaying similar documents to “An Adaptation of the Hoshen-Kopelman Cluster Counting Algorithm for Honeycomb Networks”

Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting

A. Sedki, D. Ouazar (2010)

Mathematical Modelling of Natural Phenomena

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In this paper, an artificial neural network (ANN) based on hybrid algorithm combining particle swarm optimization (PSO) with back-propagation (BP) is proposed to forecast the daily streamflows in a catchment located in a semi-arid region in Morocco. The PSO algorithm has a rapid convergence during the initial stages of a global search, while the BP algorithm can achieve faster convergent speed around the global optimum. By combining the...

Ant algorithm for flow assignment in connection-oriented networks

Krzysztof Walkowiak (2005)

International Journal of Applied Mathematics and Computer Science

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This work introduces ANB (bf Ant Algorithm for bf Non-bf Bifurcated Flows), a novel approach to capacitated static optimization of flows in connection-oriented computer networks. The problem considered arises naturally from several optimization problems that have recently received significant attention. The proposed ANB is an ant algorithm motivated by recent works on the application of the ant algorithm to solving various problems related to computer networks. However, few works concern...

Quasi-hierarchical evolution algorithm for flow assignment in survivable connection-oriented networks

Michal Przewozniczek, Krzysztof Walkowiak (2006)

International Journal of Applied Mathematics and Computer Science

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The main objective of this paper is to develop an effective evolutionary algorithm (EA) for the path-assignment problem in survivable connection-oriented networks. We assume a single-link failure scenario, which is the most common and frequently reported failure event. Since the network flow is modeled as a non-bifurcated multicommodity flow, the discussed optimization problem is NP-complete. Thus, we develop an effective heuristic algorithm based on an evolutionary algorithm. The main...

Solving maximum independent set by asynchronous distributed hopfield-type neural networks

Giuliano Grossi, Massimo Marchi, Roberto Posenato (2006)

RAIRO - Theoretical Informatics and Applications

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We propose a heuristic for solving the maximum independent set problem for a set of processors in a network with arbitrary topology. We assume an asynchronous model of computation and we use modified Hopfield neural networks to find high quality solutions. We analyze the algorithm in terms of the number of rounds necessary to find admissible solutions both in the worst case (theoretical analysis) and in the average case (experimental Analysis). We show that our heuristic is better...

Multi-agent network flows that solve linear complementarity problems

Shu Liang, Xianlin Zeng (2018)

Kybernetika

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In this paper, we consider linear complementarity problems with positive definite matrices through a multi-agent network. We propose a distributed continuous-time algorithm and show its correctness and convergence. Moreover, with the help of Kalman-Yakubovich-Popov lemma and Lyapunov function, we prove its asymptotic convergence. We also present an alternative distributed algorithm in terms of an ordinary differential equation. Finally, we illustrate the effectiveness of our method by...

A distributed transportation simplex applied to a Content Distribution Network problem

Rafaelli de C. Coutinho, Lúcia M. A. Drummond, Yuri Frota (2014)

RAIRO - Operations Research - Recherche Opérationnelle

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A Content Distribution Network (CDN) can be defined as an overlay system that replicates copies of contents at multiple points of a network, close to the final users, with the objective of improving data access. CDN technology is widely used for the distribution of large-sized contents, like in video streaming. In this paper we address the problem of finding the best server for each customer request in CDNs, in order to minimize the overall cost. We consider the problem as a transportation...

Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.

Luis M. de Campos, José A. Gámez, José M. Puerta (2002)

Mathware and Soft Computing

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The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used. A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied...