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Multi-Objective Optimal Reactive Power Dispatch of Power Systems by combining classification-Based Multi-Objective Evolutionary algorithm and Integrated Decision Making

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IEEE AccESS 2020年 8卷 38198-38209页

作者： Zhang, Meng Li, Yang Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (cPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using cPSMOEA;in the second step, integrated decision-making by combining fuzzy c-means algorithm (FcM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PIcEAg) and the third evolution step of generalized differential evolution (GDE3).

关键词： Optimization Decision making Reactive power Linear programming Evolutionary computation Security Optimal reactive power dispatch multi-objective evolutionary algorithm integrated decision-making best compromise solution fuzzy c-means algorithm grey relation projection

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A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification

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fuzzy SETS AND SYSTEMS 2005年第2期155卷 191-214页

作者： Pakhira, MK Bandyopadhyay, S Maulik, U Kalyani Govt Engn Coll Kalyani W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700035 W Bengal India Univ Jadavpur Kolkata W Bengal India

In this article, the effectiveness of variable string length genetic algorithm along with a recently developed fuzzy cluster validity index (PBMF) has been demonstrated for clustering a data set into an unknown number of clusters. The flexibility of a variable string length Genetic algorithm (VGA) is utilized in conjunction with the fuzzy indices to determine the number of clusters present in a data set as well as a good, fuzzy partition of the data for that number of clusters. A comparative study has been performed for different validity indices, namely, PBMF, XB, PE and Pc. The results of the fuzzy VGA algorithm are compared with those obtained by the well known FcM algorithm which is applicable only when the number of clusters is fixed a priori. Moreover, another genetic clustering scheme, that also requires fixing the value of the number of clusters, is implemented. The effectiveness of the PBMF index as the optimization criterion along with a genetic fuzzy partitioning technique is demonstrated on a number of artificial and real data sets including a remote sensing image of the city of Kolkata. (c) 2005 Elsevier B.V. All rights reserved.

关键词： clustering cluster validity indices fuzzy c-means algorithm genetic algorithms pixel classification variable string length

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Detection of hydrocephalus using machine learning in medical science - a review

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MULTIMEDIA TOOLS AND APPLIcATIONS 2022年第15期81卷 21199-21222页

作者： Baloni, Dev Verma, Shashi Kant Uttarakhand Tech Univ Comp Sci & Engn Sudhowala India Govind Ballabh Pant Inst Engn & Technol Pauri Garhwal India

Machine learning (ML) is the study of computer algorithms that expand spontaneously by knowledge. ML algorithms construct an analytical model centred on sample data, recognized as 'training data,' in order to make projections or conclusions without being specifically programmed to do so. Hydrocephalus is the generally known disease found in children of the central nervous system and requires neurosurgical treatment and that has been studied and imaged for years however, there is still no prevalent solution and effective method for precise detection and computable evaluation of this. This work suggests a modern form of Machine Learning (ML) for the early detection of hydrocephalus. ML is the fast growing and challenging field now days. For medical diagnosis, ML methods are used. Four phases are involved in the identification of hydrocephalus using image processing methods, namely image pre-processing, image segmentation, detection and classification of features.

关键词： Hydrocephalus Machine learning Image segmentation Mean shift clustering fuzzy c-means algorithm Feature extraction Pattern analysis conventional neural network

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A global optimization RLT-based approach for solving the fuzzy clustering problem

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JOURNAL OF GLOBAL OPTIMIZATION 2005年第4期33卷 597-615页

作者： Sherali, HD Desai, J Virginia Polytech Inst & State Univ Grado Dept Ind & Syst Engn 0118 Blacksburg VA 24061 USA

The field of cluster analysis is primarily concerned with the partitioning of data points into different clusters so as to optimize a certain criterion. Rapid advances in technology have made it possible to address clustering problems via optimization theory. In this paper, we present a global optimization algorithm to solve the fuzzy clustering problem, where each data point is to be assigned to (possibly) several clusters, with a membership grade assigned to each data point that reflects the likelihood of the data point belonging to that cluster. The fuzzy clustering problem is formulated as a nonlinear program, for which a tight linear programming relaxation is constructed via the Reformulation-Linearization Technique (RLT) in concert with additional valid inequalities. This construct is embedded within a specialized branch-and-bound (B&B) algorithm to solve the problem to global optimality. computational experience is reported using several standard data sets from the literature as well as using synthetically generated larger problem instances. The results validate the robustness of the proposed algorithmic procedure and exhibit its dominance over the popular fuzzy c-means algorithmic technique and the commercial global optimizer BARON.

关键词： clustering problem fuzzy clustering fuzzy c-means algorithm global optimization Reformulation-Linearization Technique

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A Hybrid algorithm for Location-Routing Sustainable Optimization Under fuzzy Demand

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IEEE AccESS 2022年 10卷 12191-12200页

作者： Li, Na Fan, Zhifeng Tianjin Chengjian Univ Sch Econ & Management Tianjin 300384 Peoples R China

With the globalization of the supply chain and the change of demand environment, designing an effective logistic system in the sustainable development of the supply chain becomes more critical. This study proposes a location-routing problem to determine an efficient integration of single factory and multi-distribution centers and multi-customers in uncertain demands. This problem can be regarded as an optimization integrating location, distribution decision, and routing management. The objective function is to minimize the total cost and satisfy all the requirements, which is a highly complex NP-hard problem, so a hybrid algorithm of genetic algorithm (GA) and tabu search (TS) algorithm is proposed. A fuzzy c-means clustering algorithm is used to produce an initial solution. fuzzy triangular number and confidence interval transformation are used to deal with fuzzy customer demand. The research findings concludes that (i) determine the numbers of facilities with locations that should be opened and (ii) minimize the total cost in supply chain. The experiments prove that the proposed hybrid algorithm of GA and TS algorithm overcomes the defect of local optimum in the literature viewpoint, and the optimization algorithms can effectively solve the location-routing problem.

关键词： costs clustering algorithms Supply chains Production facilities Linear programming Routing Genetic algorithms Location-routing problem fuzzy demand genetic algorithm tabu search fuzzy c-means algorithm

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Adaptive digital watermarking using fuzzy clustering technique

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IEIcE TRANSAcTIONS ON FUNDAMENTALS OF ELEcTRONIcS cOMMUNIcATIONS AND cOMPUTER ScIENcES 2001年第8期E84A卷 2052-2060页

作者： Lou, Dc Yin, TL Natl Def Univ Chung Cheng Inst Technol Dept Elect Engn Tao Yuan 33509 Taiwan

In this paper a novel adaptive digital watermarking approach based upon human visual system model and fuzzy clustering technique is proposed. The human visual system Model is utilized to guarantee that the watermarked image is imperceptible. The fuzzy clustering approach has been employed to obtain the different strength of watermark by the local characters of image. In our experiments, this scheme allows us to provide a more robust and transparent watermark.

关键词： watermarking human visual system spread-spectrum communications DcT fuzzy c-means algorithm

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fuzzy clustering of mechanisms

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SADHANA-AcADEMY PROcEEDINGS IN ENGINEERING ScIENcES 2012年第5期37卷 539-556页

作者： Ghosh, Amitabha Pratihar, Dilip Kumar Amarnath, M. V. V. Dittrich, Guenter Mueller, Jorg Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India Bengal Engn & Sci Univ Howrah 711103 India Rhein Westfal TH Aachen Inst Getriebetech & Maschinendynam D-52062 Aachen Germany

During the course of development of Mechanical Engineering, a large number of mechanisms (that is, linkages to perform various types of tasks) have been conceived and developed. Quite a few atlases and catalogues were prepared by the designers of machines and mechanical systems. However, often it is felt that a clustering technique for handling the list of large number of mechanisms can be very useful, if it is developed based on a scientific principle. In this paper, it has been shown that the concept of fuzzy sets can be conveniently used for this purpose, if an adequate number of properly chosen attributes (also called characteristics) are identified. Using two clustering techniques, the mechanisms have been classified in the present work and in future, it may be extended to develop an expert system, which can automate type synthesis phase of mechanical design. To the best of the authors' knowledge, this type of clustering of mechanisms has not been attempted before. Thus, this is the first attempt to cluster the mechanisms based on some quantitative measures. It may help the engineers to carry out type synthesis of the mechanisms.

关键词： clustering mechanisms fuzzy c-means algorithm entropy-based algorithm genetic algorithm self-organizing maps

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NEW algorithmS FOR SOLVING THE fuzzy cLUSTERING PROBLEM

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PATTERN REcOGNITION 1994年第3期27卷 421-428页

作者： KAMEL, MS SELIM, SZ KING FAHD UNIV PETR & MINERALS DEPT SYST ENGNDHAHRANSAUDI ARABIA

Two new algorithms for fuzzy clustering are presented. convergence of the proposed algorithms is proved. An empirical study of their convergence behavior is discussed. The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. Experimental results show that the new algorithms are faster and lead to computational savings.

关键词： fuzzy c-means algorithm fuzzy cLUSTERING cLUSTER ANALYSIS PATTERN REcOGNITION

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A GLOBAL algorithm FOR THE fuzzy cLUSTERING PROBLEM

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PATTERN REcOGNITION 1993年第9期26卷 1357-1361页

作者： ALSULTAN, KS SELIM, SZ Department of Systems Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

The fuzzy clustering (Fc) problem is a non-convex mathematical program which usually possesses several local minima. The global minimum solution of the problem is found using a simulated annealing-based algorithm. Some preliminary computational experiments are reported and the solution is compared with that generated by the fuzzy c-means algorithm.

关键词： fuzzy cLUSTERING fuzzy c-means algorithm cONTINUOUS SIMULATED ANNEALING NONcONVEX PROGRAMMING GLOBAL OPTIMIZATION

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fuzzy c-means clustering-based Multilayer Perceptron Neural Network for Liver cT Images Automatic Segmentation

Fuzzy C-means Clustering-based Multilayer Perceptron Neural ...

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The 22nd china control and Decision conference(2010年中国控制与决策会议)

作者： Yuqian Zhao Xiaofang Wang Yunlong Zan Guiyuan Li School of Info-Physics & Geomatics Engineering Central South University Changsha 410083 China Can School of Info-Physics & Geomatics Engineering Central South University Changsha 410083 China Cancer Research Institute Central South University Changsha 410078 China

A new liver segmentation algorithm is proposed. First, the threshold method was used to remove the ribs and spines in the initial image, and the fuzzy c-means clustering algorithm and morphological reconstruction filtering were used to segment the initial liver cT image. Then the multilayer perceptron neural network was trained by the segmentation result of initial image with the back-propagation algorithm. The adjacent slice cT image was segmented with the trained multilayer perceptron neural network. Last, morphological reconstruction filtering was used to smooth the contour of the liver edge. The experimental results show that the proposed algorithm can effectively segment the livers from cT images, despite the gray level similarity of adjacent organs and different gray level of tumors in the liver.

关键词： Liver Segmentation fuzzy c-means algorithm Multilayer Perceptron Neural Network Morphological Reconstruction Filtering

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