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Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks

EXPERT SYSTEMS WITH APPLIcATIONS 2016年 55卷 313-328页

作者： Zahedi, Zeynab Molay Akbari, Reza Shokouhifar, Mohammad Safaei, Farshad Jalali, Ali Islamic Azad Univ Fars Sci & Res Branch Dept Comp Engn Shiraz Iran Shiraz Univ Technol Dept Comp Engn & Informat Technol Shiraz Iran Shahid Beheshti Univ Dept Elect Engn Tehran Iran Shahid Beheshti Univ Fac Comp Sci & Engn Tehran *** Iran

Wireless sensor networks are rapidly evolving technological platforms with tremendous applications in several domains. Since sensor nodes are battery powered and may be used in dangerous or inaccessible environments, it is difficult to replace or recharge their power supplies. clustering is an effective approach to achieve energy efficiency in wireless sensor networks. In clustering-based routing protocols, cluster heads are selected among all sensor nodes within the network, and then clusters are formed by simply assigning each node to the nearest cluster head. The main drawback is that there is no control on the distribution of cluster heads over the network. In addition to the problem of generating unbalanced clusters, almost all routing protocols are designed for a certain application scope, and could not cover all applications. In this paper, we propose a swarm intelligence based fuzzy routing protocol (named SIF), in order to overcome the mentioned drawbacks. In SIF, fuzzy c-means clustering algorithm is utilized to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Mamdani fuzzy inference system. This strategy not only guarantees to generate balanced clusters over the network, but also has the ability to determine the precise number of clusters. In fuzzy-based routing protocols in literature, the fuzzy rule base table is defined manually, which is not optimal for all applications. Since tuning the fuzzy rules very affects on the performance of the fuzzy system, we utilize a hybrid swarm intelligence algorithm based on firefly algorithm and simulated annealing to optimize the fuzzy rule base table of SIF. The fitness function can be defined according to the application specifications. Unlike other routing protocols which have been designed for a certain application scope, the main objective of our methodology is to prolong the network lifetime based on the application specifications. In other words, SIF not onl

关键词： Wireless sensor networks clustering fuzzy c-means algorithm fuzzy inference system Firefly algorithm Simulated annealing

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Aggregated distance functions and their application in image processing

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SOFT cOMPUTING 2018年第14期22卷 4723-4739页

作者： Nedovic, Ljubo Ralevic, Nebojsa M. Pavkov, Ivan Univ Novi Sad Dept Fundamentals Sci Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Novi Sad Business Sch Higher Educ Inst Appl Studies Vladimira Perica Valtera 4 Novi Sad 21000 Serbia

In this paper, we propose a new method for construction of distance functions and metrics, by applying aggregation operators on some given distance functions and metrics. For some types and examples of aggregation operators, we analyze which properties of the given distance functions and metrics are preserved by such construction. We also present one possible application of the distance functions constructed in such way in image segmentation by fuzzy c-means algorithm. Other similar applications in image processing are also possible.

关键词： Distance function Metric Aggregation operator Image clustering fuzzy c-means algorithm

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Research on multi-source partial discharge localization based on improved FcM-LOF fuzzy clustering algorithm

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MEASUREMENT ScIENcE AND TEcHNOLOGY 2023年第1期34卷 015116-015116页

作者： Hu, Xiaoxu Wang, Jingang Xu, changjian Yan, Xiaojun Zhao, Pengcheng Liu, Ya Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 40044 Peoples R China

At present, the ultra-high frequency method is widely used in the single-source partial discharge (PD) location of substation sites;however, there are issues with the positioning accuracy being too low, and it is difficult to meet the needs of multi-source PD positioning. Based on the local outlier factor (LOF) outlier idea and fuzzy c-means (FcM) clustering algorithm, this paper proposes the FcM-LOF algorithm to be applied to the research of multi-source PD positioning. This algorithm removes the discrete points of the original time difference data set based on the principle of the local density threshold, reduces the clustering center error, and improves the accuracy of multi-source positioning. The main research contents of this article are as follows. Firstly, using smoothing filter processing and the energy accumulation method, we collect the time difference of dual- and triple-source PD signals as the data set to be processed, and carry out the laboratory simulation experiment. Secondly, comparing the clustering effects of the k-means and FcM algorithms, it is found that the clustering accuracy of the FcM algorithm is significantly better than that of the k-means algorithm, and the positioning error is reduced by 27.3%. Then, using the neighborhood density and outlier factor to eliminate abnormal data, combined with FcM fuzzy clustering, an improved FcM-LOF algorithm is proposed. compared with the FcM algorithm, the positioning error of this algorithm is reduced by 11.6%. It is suitable for multi-source positioning and has a greater improvement in noisy environments. Finally, the improved algorithm is applied to a field simulation test, which verifies the accuracy of the algorithm. We also study the factors affecting the positioning accuracy of the improved algorithm. The research in this paper can provide a powerful reference for multi-source PD detection of power equipment.

关键词： multi-source partial discharge ultra-high frequency method fuzzy c-means algorithm local outlier factor simulation test positioning accuracy

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Hybridization of Particle Swarm Optimization with Unsupervised clustering algorithms for Image Segmentation

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INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2008年第3期10卷 217-230页

作者： Liu, Wenping McGrath, Ethan Hung, chih-cheng Kuo, Bor-chen Beijing Forestry Univ Beijing Peoples R China

Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The fuzzy c-means algorithm (FcM) and the Possibilistic c-means algorithm (PIcA) have been widely used. There is also the generalized possibilistic algorithm (GPcA). GPcA was proposed recently and is a general form of the previous algorithms. These clustering algorithms can be trapped to the local optimal solutions. Hence, optimization techniques are often used in conjunction with algorithms to improve the performance. Some of optimization techniques have been inspired by nature such as swarm behavior. Particle Swarm Optimization (PSO) is one such technique. In this paper, PSO heuristics were combined with FcM, PIcA, and GPcA algorithms to improve the overall clustering accuracy of these algorithms. To test the improvement with the PSO, these algorithms were tested on images. The overall effect of adding unique PSO methods was a higher percentage of satisfactory image segmentations.

关键词： fuzzy c-means algorithm Possibilistic clustering algorithms Particle Swarm Optimization

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On post-clustering evaluation and modification

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PATTERN REcOGNITION LETTERS 2000年第5期21卷 365-373页

作者： Ong, SH Zhao, X Natl Univ Singapore Dept Elect Engn Singapore 119260 Singapore

Unsupervised clustering algorithms sometimes do not lead to meaningful interpretations of the structure in the data. We propose a new approach in which the concept of cluster density is introduced to assess the quality of an algorithmically generated partition and accordingly guide an amelioration process through split-and-merge operations. (c) 2000 Published by Elsevier Science B.V. All rights reserved.

关键词： clustering density cluster validity image segmentation fuzzy c-means algorithm

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A tabu search-based algorithm for the fuzzy clustering Problem

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PATTERN REcOGNITION 1997年第12期30卷 2023-2030页

作者： Al-Sultan, KS Fedjki, cA King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

The fuzzy clustering Problem (FcP) is a mathematical program which is difficult to solve since it is nonconvex, which implies possession of many local minima. The fuzzy c-means heuristic is the widely known approach to this problem, but it is guaranteed only to yield local minima. In this paper, we propose a new approach to this problem which is based on tabu search technique, and aims at finding a global solution of FcP. We compare the performance of the algorithm with the fuzzy c-means algorithm. (c) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.

关键词： fuzzy clustering fuzzy c-means algorithm tabu search technique global optimization

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fuzzy Bayesian validation for cluster analysis of yeast cell-cycle data

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PATTERN REcOGNITION 2006年第12期39卷 2405-2414页

作者： cho, Sung-Bae Yoo, Si-Ho Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

clustering for the analysis of the genes organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster and analyzing the functions of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to only one group. In this paper, a Bayesian-like validation method selecting a fuzzy partition is proposed to evaluate the fuzzy partitions effectively. The theoretical interpretation of the obtained memberships is beyond the scope of this paper, and an empirical evaluation of the proposed method is conducted by comparing to the four representative conventional fuzzy cluster validity measures in four well-known datasets. Analysis of yeast cell-cycle data follows to evaluate the proposed method. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： fuzzy clustering fuzzy c-means algorithm fuzzy Bayesian validation method yeast cell-cycle data

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copula-Based Pooled Frequency Analysis of Droughts in the canadian Prairies

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JOURNAL OF HYDROLOGIc ENGINEERING 2014年第2期19卷 277-289页

作者： Sadri, Sara Burn, Donald H. Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

This paper studies the frequency analysis of droughts using a copula with the application of regionalization in the context of a bivariate homogeneity analysis. Drought events indicated by severity and duration were extracted from monthly flow averages. A K-means clustering algorithm was used to form initial regions. A fuzzy c-means algorithm was used to form the final groups of sites that meet the criteria of bivariate discordancy, bivariate homogeneity, and size. The application of the Gumbel, clayton, and Frank copulas for bivariate drought frequency analysis was studied. Results show the importance of a clear definition of drought in every scenario since, in our example, the longest drought does not necessarily correspond to the most severe one. Another important observation of this study was that, given the average annual rainfall of a catchment, droughts seem to occur in almost all regions, humid or arid. However, areas with higher annual rainfall can experience shorter but more severe drought. The procedures of this study are applicable for flood frequency analysis as well. Furthermore, ungauged sites can be integrated in the procedure of regionalization.

关键词： Regionalization Regional drought frequency analysis fuzzy c-means algorithm Drought Bivariate discordancy Bivariate homogeneity L-comoments

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ON THE LOcAL OPTIMALITY OF THE fuzzy ISODATA cLUSTERING-algorithm

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IEEE TRANSAcTIONS ON PATTERN ANALYSIS AND MAcHINE INTELLIGENcE 1986年第2期8卷 284-288页

作者： SELIM, SZ ISMAIL, MA UNIV WINDSOR SCH COMP SCIWINDSOR N9B 3P4ONTARIOCANADA

The convergence of the fuzzy ISODATA clustering algorithm was proved by Bezdek [3]. Two sets of conditions were derived and it was conjectured that they are necessary and sufficient for a local minimum point. In this paper, we address this conjecture and explore the properties of the underlying optimization problem. The notions of reduced objective function and improving and feasible directions are used to examine this conjecture. Finally, based on the derived properties of the problem, a new stopping criterion for the fuzzy ISODATA algorithm is proposed.

关键词： clustering algorithms Hilbert space Filtering Speech processing Multidimensional signal processing Systems engineering and theory Kalman filters Signal processing algorithms Petroleum Minerals Author Keywords local optimality fuzzy clustering algorithms fuzzy c-means algorithm fuzzy ISODATA algorithm fuzzy unsupervised classification

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Simulation-Enhanced MQAM Modulation Identification in communication Systems: A Subtractive clustering-Based PSO-FcM algorithm Study

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INFORMATION 2024年第1期15卷 42-42页

作者： Quan, Zhi Zhang, Hailong Luo, Jiyu Sun, Haijun Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FcM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FcM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FcM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the Sc-based PSO-FcM algorithm outperforms the conventional FcM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.

关键词： modulation recognition fuzzy c-means algorithm constellation diagram particle swarm optimization algorithm MQAM signal

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