scholarly journals Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2255
Author(s):  
Krzysztof Przystupa ◽  
Julia Pyrih ◽  
Mykola Beshley ◽  
Mykhailo Klymash ◽  
Andriy Branytskyy ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Random Search ◽  
Signal Propagation ◽  
Propagation Model ◽  
Route Search ◽  
Annealing Algorithms ◽  
Self Organizing Networks ◽  
Self Organizing

With the constant growth of requirements to the quality of infocommunication services, special attention is paid to the management of information transfer in wireless self-organizing networks. The clustering algorithm based on the Motley signal propagation model has been improved, resulting in cluster formation based on the criterion of shortest distance and maximum signal power value. It is shown that the use of the improved clustering algorithm compared to its classical version is more efficient for the route search process. Ant and simulated annealing algorithms are presented to perform route search in a wireless sensor network based on the value of the quality of service parameter. A comprehensive routing method based on finding the global extremum of an ordered random search with node addition/removal is proposed by using the presented ant and simulated annealing algorithms. It is shown that the integration of the proposed clustering and routing solutions can reduce the route search duration up to two times.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

scholarly journals Heterogeneous Simulated Annealing Teams: An Optimizing Search Algorithm Inspired by Engineering Design Teams

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Engineering Design ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Design Teams ◽  
Design Cognition ◽  
Gradient Based ◽  
Multi Agent ◽  
Annealing Algorithms

Although insights uncovered by design cognition are often utilized to develop the methods used by human designers, using such insights to inform computational methodologies also has the potential to improve the performance of design algorithms. This paper uses insights from research on design cognition and design teams to inform a better simulated annealing search algorithm. Simulated annealing has already been established as a model of individual problem solving. This paper introduces the Heterogeneous Simulated Annealing Team (HSAT) algorithm, a multi-agent simulated annealing algorithm. Each agent controls an adaptive annealing schedule, allowing the team develop heterogeneous search strategies. Such diversity is a natural part of engineering design, and boosts performance in other multi-agent algorithms. Further, interaction between agents in HSAT is structured to mimic interaction between members of a design team. Performance is compared to several other simulated annealing algorithms, a random search algorithm, and a gradient-based algorithm. Compared to other algorithms, the team-based HSAT algorithm returns better average results with lower variance.

scholarly journals Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

2016 ◽  
Vol 16 (6) ◽  
pp. 27-42 ◽  
Author(s):  
Minghan Yang ◽  
Xuedong Gao ◽  
Ling Li
Keyword(s):  
Simulated Annealing ◽  
Data Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Parameter Determination ◽  
Data Sets ◽  
Parameter Vector ◽  
Clustering Validity

Abstract Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

scholarly journals Simulated Annealing with Exploratory Sensing for Global Optimization

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Majid Almarashi ◽  
Wael Deabes ◽  
Hesham H. Amin ◽  
Abdel-Rahman Hedar
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Initial Temperature ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Algorithms ◽  
Two Phases ◽  
Number Of Iterations

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.

Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map

2014 ◽  
Vol 1039 ◽  
pp. 538-543
Author(s):  
Lelija Stupar ◽  
Quan Yu ◽  
Ke Sheng Wang
Keyword(s):  
Supervised Classification ◽  
Clustering Algorithm ◽  
Geometric Features ◽  
Quality Inspection ◽  
Self Organizing Map ◽  
Improve Quality ◽  
Chi Square ◽  
Automatic Interaction Detector ◽  
Self Organizing

This paper describes two methods for the industrial quality inspection: Supervised classification algorithm Chi-Square Automatic Interaction Detector (CHAID) and unsupervised clustering algorithm Self-Organizing Map (SOM). The classification and clustering are modelled in IBM software SPSS. Models’ functioning is illustrated on a wheel assembly geometric features inspection. The classifying accuracies are compared for the two methods. CHAID has shown better classifying ability than SOM, while SOM can be used to improve quality of predictor values, and therefore classifiers accuracy.

scholarly journals Assessment and Modeling of GSM Signal Propagation in Uyo, Nigeria

2017 ◽  
Vol 2 (11) ◽  
pp. 49 ◽  
Author(s):  
Sunny Orike ◽  
Promise Elechi ◽  
Iboro Asuquo Ekanem
Keyword(s):  
Field Strength ◽  
Path Loss ◽  
Signal Propagation ◽  
Empirical Models ◽  
Propagation Model ◽  
Link Quality ◽  
Base Stations ◽  
Quality Factors ◽  
Irregular Terrain

High quality of service is a paramount concern in wireless networks. One of the strategies in achieving optimal performance is to use wireless empirical models to predict wireless link quality factors such as path loss and the received power in any given transmission domain with irregular terrain. The primary goal of this study is to develop a radio wave propagation model for Uyo metropolis. An assessment was carried out in three major roads within the city of Uyo in Akwa Ibom State, to determine the quality of GSM signal reception by measuring the signal field strength, magnetic field strength, and power density of the base transceiver stations. The measurements were carried out using radio frequency electromagnetic field strength meter over a distance of 2000 meters from the base stations. The results of the measurements were analysed and a path loss model was developed for Uyo using linear regression model. Three empirical models: Okumura-Hata model, COST-Hata model, and Egli model were also applied in predicting the path loss in Uyo and the results obtained were compared with the developed model for Uyo metropolis. The comparison showed that Route D model had a better comparison factor with the developed model while the Okumura-Hata and COST-Hata were almost the same with more loss as the distance increased. In all the measurements, the standard deviation was between 3.31 dB and 3.36 dB.

An Improved Simulated Annealing Algorithm for Optimal Design

1995 ◽  
Author(s):  
Sherif Aly ◽  
Madara M. Ogot ◽  
Richard Pelz
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Simulated Annealing Algorithm ◽  
Random Search ◽  
Computation Time ◽  
Global Optimum ◽  
Search Method ◽  
Local Search Method ◽  
Iterative Improvement

Abstract A new algorithm based on the simulated annealing (SA) optimization algorithm is presented. This approach, simulated annealing with random search iterative improvement (SAWI), essentially initiates the SA process to locate the neighborhood of the global optimum. Prior to the convergence of SA, the algorithm switches to random search iterative improvement, a local search method, to converge to the optimum. The key to the effectiveness of SAWI is identifying when the premature termination of SA should occur. This paper presents the results of a parametric study conducted on the transition parameter, illustrating the effects of delayed and premature transition to the local search method, on the final solution. Two examples are presented and discussed to illustrate the efficacy of the algorithm. The results of these examples demonstrate that SAWI makes significant reductions in computation time while maintaining the simplicity of the original SA algorithm and without loss in quality of solution.

Application of Fractal Methods to Ensure the Cyber-Resilience of Self-Organizing Networks

2019 ◽  
Vol 22 (4) ◽  
pp. 336-341
Author(s):  
D. V. Ivanov ◽  
D. A. Moskvin
Keyword(s):  
Information Security ◽  
Network Structure ◽  
Self Regulation ◽  
Security Threats ◽  
Self Organizing Networks ◽  
Self Organizing ◽  
Fractal Methods

In the article the approach and methods of ensuring the security of VANET-networks based on automated counteraction to information security threats through self-regulation of the network structure using the theory of fractal graphs is provided.

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