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检索条件"主题词=binary Particle Swarm Optimization"

共 330 条记录，以下是251-260 订阅

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Optimal Mapping of DG Units in Island Partitioning Mode under Fault Condition Using BPSO: A Case Study of Thadi Feeder of Nepal

Optimal Mapping of DG Units in Island Partitioning Mode unde...

引用

IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Khatri, Pradip Khanal, Asha Tribhuvan Univ Dept Elect Engn Pulchowk Campus Lalitpur Nepal

ISBN: (纸本)9781665442312

The distribution system is one of the most crucial parts of a power system. In case of any emergency fault condition in the network, the islanding operation with available Distributed Generation (DG) will maintain the availability/ reliability of the system within acceptable limit. The available DGs must be allocated optimally such that maximum load will be restored in the system. The purpose of this study is to determine the best approach for restoring the post-fault distribution system. It utilizes the graphical algorithm to assign the load to the DGs and perform the load shedding problem using binary particle swarm optimization (BPSO), if there is any shortage of the power supply in the system. Also, the load flow analysis is also performed to check the feasibility of the obtained optimal mapping of DGs. This paper properly takes into account the load priorities factors, voltage limit, power imbalance issues and the line capacity requirements for the optimal operation of system. The methodology is applied to rural radial distribution network of Nepal Electricity Authority (NEA), Thadi feeder, Nepal. and the results reflects the optimal maximum restoration of the load during post fault conditions.

关键词： Islanding Distributed Generation binary particle swarm optimization Restoration Power flow

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Optimizing Deployment Cost in Camera-Based Wireless Sensor Networks 5

Optimizing Deployment Cost in Camera-Based Wireless Sensor N...

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5th IFIP TC 5 International Conference on Computer Science and Its Applications (CIA)

作者： Serik, Mehdi Rouan Kaddour, Mejdi Univ Oran 1 LITIO Lab BP 1524 El Mnaouer 31000 Oran Algeria

ISBN: (纸本)9783319195780;9783319195773

We discuss in this paper a deployment optimization problem in camera-based wireless sensor networks. In particular, we propose a mathematical model to solve the problem of minimizing the number of cameras required to cover a set of targets with a given level of quality. Since solving this kind of problems with exact methods is computationally expensive, we rather rely on an adapted version of binary particle swarm optimization (BPSO). Our preliminary results are motivating since we obtain near-optimal solutions in few iterations of the algorithm. We discuss also the relevance of hybrid meta-heuristics and parallel algorithms in this context.

关键词： Camera-based wireless sensor networks Minimum cost deployment Coverage quality binary particle swarm optimization

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Optimal Probabilistic PMU Placement in Electric Distribution System State Estimation 10

Optimal Probabilistic PMU Placement in Electric Distribution...

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10th IEEE International Workshop on Applied Measurements for Power Systems (AMPS)

作者： Arefi, Ali Haghifam, Mahmood-Reza Fathi, Seyed-Hamid Behi, Behnaz Razavi, S. Ehsan Jennings, Philip Murdoch Univ Discipline Engn & Energy Perth WA Australia Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781728100753

This paper presents an algorithm for optimal placement of phasor measurement units (PMUs) in electric distribution networks to obtain predefined probabilistic relative errors in magnitude and angle for a distribution system state estimation (DSSE). The probabilistic relative error is introduced to consider the effect of failure probability (FP) and sending bad data probability (BDP) of PMUs. In this paper, the probabilistic relative error is defined as the expected value of the relative error values corresponding to the operating states of PMUs, which are calculated from Monte Carlo simulations. The binary particle swarm optimization (BPSO) is adapted to find the optimal number and locations of PMUs in a distribution network. The simulation results on 6-bus and 34-bus IEEE radial distribution networks show the effect of FP and BDP on the PMU placement as well as the performance of the proposed algorithm.

关键词： Distribution network PMU State estimation binary particle swarm optimization Probabilistic relative error Optimal PMU placement

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BPSOBDE: A binary Version of Hybrid Heuristic Algorithm for Multidimensional Knapsack Problems 13

BPSOBDE: A Binary Version of Hybrid Heuristic Algorithm for ...

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Zhang, Li Li, Hong Xidian Univ Sch Math & Stat Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538648223

A hybrid heuristic algorithm, named BPSOBDE, is proposed for solving multidimensional knapsack problems (MKPs), in which the basic binary particle swarm optimization (BPSO) is combined with a binary differential evolution (BDE) to maintain the diversity of the swarm and makes it more explorative, effective and efficient. BPSOBDE is tested through computational experiments over suites of benchmark problems and the obtained results are compared with those of BPSO and some modified versions of BPSO. The experimental results show that BPSOBDE can successfully locate the exact solutions of all test problems. The comparison results indicate that BPSOBDE is a competitive heuristic algorithm.

关键词： Knapsack problems binary particle swarm optimization binary differential evolution Hybrid heuristic algorithm

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Mining Top-K High Utility Itemset Using Bio-Inspired Algorithms

Mining Top-K High Utility Itemset Using Bio-Inspired Algorit...

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2022 Workshop on Complexity in Engineering

作者： Nam Ngoc Pham Oplatkova, Zuzana Kominkova Huy Minh Huynh Bay Vo Tomas Bata Univ Fac Appl Informat Zlin Czech Republic HUTECH Univ Ho Chi Minh City Vietnam

ISBN: (数字)9781728171241

ISBN: (纸本)9781728171241

High utility itemset (HUI) mining is a necessary research problem in the field of knowledge discovery and data mining. Many algorithms for Top-K HUI mining have been proposed. However, the principal issue with these algorithms is that they need to store potential top-k patterns in the memory anytime, and they request the minimum utility threshold to automatically rise when finding HUIs. Consequently, the performance of existing exact algorithms for Top-K HUIs mining tends to decrease when the database size and the number of distinct items in the databases rise. To address this issue, we suggest a binary particle swarm optimization (BPSO) based algorithm for mining Top-K HUIs effectively, namely TKO-BPSO (Top-K high utility itemset mining in One phase based on binary particle swarm optimization). The main idea of TKO-BPSO is not only to use a one-phase model and strategy Raising the threshold by the Utility of Candidates (RUC) to effectively increase the border thresholds for pruning the search space but also to adopt the sigmoid function in the updating process of the particles. This might significantly reduce the combinational problem in traditional HUIM when the database size and the number of distinct items in the databases rise. Consequently, its performance outperforms existing exact algorithms for mining Top-K HUIs because it efficiently overcomes the problem of the vast amount candidates. Substantial experiments conducted on publicly available several real and synthetic datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime, which can significantly reduce the combinational problem and memory usage.

关键词： bio-inspired algorithm top-k high utility itemset mining binary particle swarm optimization

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Ensemble Classifiers Based on Kernel PCA for Cancer Data Classification

Ensemble Classifiers Based on Kernel PCA for Cancer Data Cla...

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5th International Conference on Intelligent Computing

作者： Zhou, Jin Pan, Yuqi Chen, Yuehui Liu, Yang Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9783642040191

Now the classification of different tumor types is of great importance in cancer diagnosis and drug discovery. It is more desirable to create an optimal ensemble for data analysis that deals with few samples and large features. In this paper, a new ensemble method for cancer data classification is proposed. The gene expression data is firstly preprocessed for normalization. Kernel Principal Component Analysis (KPCA) is then applied to extract features. Secondly, an intelligent approach is brought forward, which uses Support Vector Machine (SVM) as the base classifier and applied with binary particle swarm optimization (BPSO) for constructing ensemble classifiers. The leukemia and colon datasets are used for conducting all the experiments. Results show that the proposed method produces a good recognition rate comparing with some other advanced artificial techniques.

关键词： Cancer data classification Kernel principal component analysis Support vector machine Ensemble classifier binary particle swarm optimization

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A hybrid approach for selecting gene subsets using gene expression data

A hybrid approach for selecting gene subsets using gene expr...

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IEEE Conference on Soft Computing in Industrial Applications

作者： Yang, Cheng-San Chuang, Li-Yeh Ke, Chao-Hsuan Yang, Cheng-Hong Inst Biomed Engn Tainan Taiwan I Shou Univ Dept Chem Engn Kaohsiung 84001 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80778 Taiwan

ISBN: (纸本)9781424437825

Many previous research papers have demonstrated that microarray gene expression data are useful for disease classification and medical diagnosis. Cancer microarray data normally have a particular characteristic where features (genes) greatly exceed the instance (tissue sample) numbers. Selecting appropriate numbers and relevant features to differentiate different types of cancer remains a challenge in bioinformatics. In order to select useful gene sets from microarray data to promote classification performance effectively, feature selection approaches were included in many previous literature reports. In this paper, a hybrid approach which combines correlation-based feature selection and binary particle swarm optimization was used to select few subsets, combined with the K-nearest neighbor method as a classifier to evaluate the classification performance. The proposed approach is applied on six microarray gene expression data sets that relate to human cancer. The experimental results show that the proposed approach selects a smaller number of feature subsets and obtains better classification accuracy.

关键词： Microarray gene expression data Feature selection correlation-based feature selection binary particle swarm optimization

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Face Recognition using Dual Wavelet Transform and Filter-Transformed Flipping

Face Recognition using Dual Wavelet Transform and Filter-Tra...

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International Conference on Electronics and Communication Systems (ICECS)

作者： Khashu, Sagar Vijayanagar, Sowjanya Manikantan, K. Ramachandran, S. MS Ramaiah Inst Tech Dept Elect & Commun Engn Bangalore 560054 Karnataka India SJB Inst Tech Dept Elect & Commun Engn Bangalore 560060 Karnataka India

ISBN: (纸本)9781479966387

Face Recognition (FR) under varying pose and illumination conditions is challenging, and extracting pose and illumination invariant features is an effective approach to solve this problem. In this paper, we propose three novel techniques, viz. Filtered Dual Wavelet Transform (FDuWT), Raster Scan Discrete Wavelet Transform (RDWT) and Filter Transformed Flipping (FTF), to improve the performance of the FR system. FDuWT is used for edge enhancement and denoising. RDWT is used to extract salient features. FTF is used to neutralize the pose variant features. Individual stages of the FR system are examined and an attempt is made to improve each stage. After efficient feature extraction through RDWT, a binary particle swarm optimization based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on three benchmark face databases, namely, Color FERET, Extended Yale B and Pointing Head Pose, show that the proposed system outperforms other FR systems.

关键词： Face Recognition Image Preprocessing Feature Extraction binary particle swarm optimization

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Face Recognition Using Snakes Algorithm and Skin Detection Based Face Localization

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International Conference on Signal, Networks, Computing, and Systems (ICSNCS)

作者： Ramesh, Rakshit Kulkarni, Anoop C. Prasad, N. R. Manikantan, K. MS Ramaiah Inst Technol Dept Elect & Commun Engn Bangalore 560054 Karnataka India

ISBN: (纸本)9788132235927;9788132235903

Skin detection is an important part of face localization since the most exposed part of human skin is the face. This paper proposes a novel algorithm for face localization via skin detection. The algorithm utilizes a kernel iterative procedure to check for the region of interest in an image where it is likely that the face exists. The algorithm utilizes a swarm of particles in a kernel that randomly check the fitness of the corresponding pixel BGR value which is determined from a skin BGR dataset. Consequently, the kernel which has the best fitness value is chosen as the region of the image where it is likely that the face exists. Following this, we employ an active contour model called Snakes algorithm which further converges on our region of interest and finally a contour of the face region is extracted from the original image.

关键词： Face localization swarm intelligence Face recognition binary particle swarm optimization Snakes algorithm

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Iris Recognition using Radon Transform Thresholding based Feature Extraction with Gradient-based Isolation as a Pre-processing Technique 9

Iris Recognition using Radon Transform Thresholding based Fe...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Bharath, B. V. Vilas, A. S. Manikantan, K. Ramachandran, S. MS Ramaiah Inst Tech Dept Elect & Commun Engn Bangalore 560054 Karnataka India SJB Inst Tech Dept Elect & Commun Engn Bangalore 560060 Karnataka India

ISBN: (纸本)9781479964994

Iris Recognition (IR) under varying contrast and live-tissues is challenging. This paper proposes two novel techniques viz., Radon Transform Thresholding (RTT) and Gradient based Isolation (GI). RTT is used to extract the prominent features from the pre-processed image. GI is a pre-processing technique which uses the edge detection property of Gradient operator to isolate the patterns, thereby obtaining the salient Iris textures. A binary particle swarm optimization (BPSO) based feature selection algorithm is used to search the feature vector space for the optimal feature subset. Experimental results on three benchmark Iris databases, namely, Phoenix, IITD and CASIA Iris Interval, depict promising performance of the proposed techniques for Iris recognition.

关键词： Iris Recognition Feature Extraction Feature Selection Radon Transform binary particle swarm optimization

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