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

共 332 条记录，以下是61-70 订阅

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A Distributed Collaborative Dynamic Weapon-Target Assignment Method Based on Improved binary particle swarm optimization Algorithm

A Distributed Collaborative Dynamic Weapon-Target Assignment...

引用

International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Xu, Shengli Liu, Yintian Zhang, Hongcheng Wei, Yali Fang, Yangwang Northwestern Polytech Univ Xian 710072 Peoples R China Shanghai Electromech Engn Inst Shanghai 201109 Peoples R China Shanghai Acad Spaceflight Technol Shanghai 201109 Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

The Weapon-Target Assignment (WTA) problem is discussed in this study using air-to-air missiles as the research subject. A weapon target assignment model that can be used with air-to-air missiles is later proposed when a threat assessment model is first built. Additionally, the target assignment model is resolved using the binary particle swarm optimization (BPSO). Then a simulation of a typical fighting scenario was created. The target assignment model is effectively specified, the algorithm is solved consistently, and the algorithm converges quickly, according to the simulation statistics. The approach establishes the groundwork for solving the later missile weapon-target assignment problem.

关键词： binary particle swarm optimization Weapon-target assignment Cooperative dynamic assignment Threat assessment

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Solving the Unit Commitment Problem with Improving binary particle swarm optimization 13th

Solving the Unit Commitment Problem with Improving Binary Pa...

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13th International Conference on swarm Intelligence (ICSI)

作者： Liu, Jianhua Wang, Zihang Chen, Yuxiang Zhu, Jian Fujian Univ Technol Sch Informat Sci & Engn Fuzhou 350108 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

Unit commitment is a traditional mixed-integer non-convex problem and an optimization task in power system scheduling. The traditional methods of solving the Unit commitment problem have some problems, such as slow solving speed, low accuracy and complex calculation. Therefore, intelligent algorithms have been applied to solve the unit combination problem with continues and discrete feature, such as particle swarm optimization, Genetic Algorithm. In order to improve the solution quality of Unit commitment, this paper proposes the adaptive binary particle swarm optimization with V-shaped transfer function to solve the unit commitment problem, and adopts the policy of the segmented solution. By comparison with some classical algorithm in the same unit model, the experimental results show that solving the UC problem by using improved algorithm with segmented solution has higher stability and lower total energy consumption.

关键词： binary particle swarm optimization Segmented solution Unit commitment problem

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Intrusion Detection Algorithms based on Correlation Information Entropy and binary particle swarm optimization 13

Intrusion Detection Algorithms based on Correlation Informat...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Wang, Yan-fei Liu, Pei-yu Ren, Min Chen, Xiao-xue Shandong Normal Univ Sch Informat Sci & Engn Shandong Prov Key Lab Distributed Comp Software N Jinan Shandong Peoples R China Shandong Univ Finance & Econ Sch Math & Quantitat Econ Shandong Prov Key Lab Distributed Comp Software N Jinan Shandong Peoples R China

ISBN: (纸本)9781538621653

In current intrusion detection, redundant features often lead to the degradation of detection accuracy. Aiming at this problem, an intrusion detection algorithm based on correlation information entropy and binary particle swarm optimization algorithm was proposed. Correlation information entropy was used to sort features. This can filter irrelevant features. So the feature dimension was reduced. Then some better subsets that were gotten from feature sorting were used as the part initial population. In this way, the following particle swarm optimization algorithm would have a good starting point. The test results showed that the better classification performance was obtained according to the selected optimal feature subset, and the testing time of the system was reduced effectively.

关键词： feature selection intrusion detection correlation information entropy binary particle swarm optimization support vector machine

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The Improvement of V-Shaped Transfer Function of binary particle swarm optimization 11th

The Improvement of V-Shaped Transfer Function of Binary Part...

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11th International Conference on swarm Intelligence (ICSI)

作者： Zhang, Dong-Yang Liu, Jian-Hua Jiang, Lei Bu, Guan-Nan Hu, Ren-Yuan Luo, Yi-Xuan Fujian Univ Technol Sch Informat Sci & Engn Fuzhou Peoples R China Fujian Prov Key Lab BigData Min & Applicat Fuzhou Peoples R China

ISBN: (纸本)9783030539559;9783030539566

binary particle swarm optimization (BPSO) is a swarm intelligence to optimize discrete space problems by extending the particle swarm optimization. Its transfer function is the key element of BPSO. In this paper, a new V-shaped transfer function with a parameter k has been proposed. The parameter k was used to control the opening size of the transfer function. At first, the setting of the parameter k has been obtained by the experiments, and then the new V-shaped transfer with the optimal k value is compared with the other kinds of the V-shaped transfer functions by the experiment of feature selection. The results have indicated that the new V-shaped transfer function improved the performance of binary particle swarm optimization.

关键词： binary particle swarm optimization V-shaped transfer function Classification accuracy Iterative variation of distance between particles

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An Improved Energy-Aware Cluster Heads Selection Method for Wireless Sensor Networks Based on K-means and binary particle swarm optimization 5

An Improved Energy-Aware Cluster Heads Selection Method for ...

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5th International Conference on swarm Intelligence (ICSI)

作者： Pan, Qianqian Ni, Qingjian Du, Huimin Yao, Yiyun Lv, Qing PLA Univ Sci & Technol Lab Mil Network Technol Nanjing Jiangsu Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Coll Software Engn Nanjing Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319118970;9783319118963

The limited and non-replenishable energy supply is the main character of Wireless Sensor Networks (WSNs). Hence, maximizing the lifetime of WSNs becomes a critical issue in sensor networks. Clustering is one of the most effective means to extend the lifetime of the whole network. In this paper, an energy-aware cluster heads selection method, based on binary particle swarm optimization (BPSO) and K-means, is presented to prolong the network lifetime. We apply the BPSO and make it suitable for this issue. The selection criteria of the objective cost function are based on minimizing the intra-cluster distance as well as the distance between cluster heads and base station, and optimizing the energy consumption of the whole network. In addition, the sensor nodes are divided into several clusters based on K-means algorithm at the beginning, which can reduce the complexity of the whole algorithm. The performance of our technique is compared with the well-known cluster-based sensor network protocols, LEACH-C and PSO-C respectively. The simulation results demonstrate that our proposed work can achieve better network lifetime over its comparatives.

关键词： Wireless Sensor Networks binary particle swarm optimization Energy-aware Cluster K-means Algorithm

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Implementation of binary particle swarm optimization for DNA Sequence Design

引用

10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Khalid, Noor Khafifah Ibrahim, Zuwairie Kurniawan, Tri Basuki Khalid, Marzuki Engelbrecht, Andries P. Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Johor Malaysia Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

ISBN: (纸本)9783642024801

In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence design problem is a multi-objective problem, and it can be evaluated using four objective functions, namely, H-measure, similarity, continuity, and hairpin. There are several ways to solve a multi-objective problem, such as value function method, weighted sum method, and using evolutionary algorithms. However, in this paper, common method has been used, namely weighted sum method to convert DNA sequence design problem into single objective problem. binary particle swarm optimization (BinPSO) is proposed to minimize the objective in the problem, subjected to two constraints: melting temperature and GC(content), Based on experiments and researches done, 20 particles are used in the implementation of the optimization process, where the average values and the standard deviation for 100 runs are shown along with comparison to other existing methods. The results obtained verified that BinPSO can suitably solve DNA sequence design problem using the proposed method and model, comparatively better than other approaches.

关键词： binary particle swarm optimization DNA sequence design optimization

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Detection of Heart Disease using binary particle swarm optimization

Detection of Heart Disease using Binary Particle Swarm Optim...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Elbedwehy, Mona Nagy Zawbaa, Hossam M. Ghali, Neveen Hassanien, Aboul Ella Mansoura Univ Egypt SRGE Dept Math Fac SciDamietta Branch Mansoura Egypt Cairo Univ Fac Computers & Informat Cairo Egypt Al Azhar Univ Egypt Sci Res Grp Egypt SRGE Fac Sci Cairo Egypt

ISBN: (纸本)9788360810484

This article introduces a computer-aided diagnosis system of the heart valve disease using binary particle swarm optimization and support vector machine, in conjunction with K-nearest neighbor and with leave-one-out cross-validation. The system was applied in a representative heart dataset of 198 heart sound signals, which come both from healthy medical cases and from cases suffering from the four most usual heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). The introduced approach starts with an algorithm based on binary particle swarm optimization to select the most weighted features. This is followed by performing support vector machine to classify the heart signals into two outcome: healthy or having a heart valve disease, then its classified the having a heart valve disease into four outcomes: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). The experimental results obtained, show that the overall accuracy offered by the employed approach is high compared with other techniques

关键词： binary particle swarm optimization Support vector machine Heart valve diseases Heart sounds

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Optimum Nutrition Intake from Daily Dietary Recommendation for Indonesian Children Using binary particle swarm optimization Algorithm 4

Optimum Nutrition Intake from Daily Dietary Recommendation f...

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4th International Conference on Computer Science and Computational Intelligence (ICCSCI) - Enabling Collaboration to Escalate Impact of Research Results for Society

作者： Fanny Wulandhari, Lili Ayu Isa, Sani Muhamad Bina Nusantara Univ Sch Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Bina Nusantara Univ Binus Grad Program Master Comp Sci Comp Sci Dept Jakarta 11480 Indonesia

Optimum nutrition intake in daily dietary habit has a significant role for children growth. Nevertheless, the mistakenness in the fulfillment of nutrition still concerned. It happens because an individual does not have much knowledge about the energy content of food and food combination to meet the nutrition requirement. The objectives of this research are to facilitate an individual to obtain the optimum nutrition intake from their daily dietary habit. This paper proposes a binary particle swarm optimization (BPSO) algorithm to fmd the optimum combination of food portion and food option for an individual daily dietary habit. The food data is obtained from 'Tabel Komposisi Pangan Indonesia' book which contains more than 1600 kind of Indonesian food. The results show that BPSO provides an optimum nutrition intake accuracy of 99.14%. Moreover, the nutritionist is already validated that this experiment is succeed in recommending a variation of daily dietary habit that meet an optimum nutrition intake for an individual. Based on this result it can be conducted that BPSO can provide the better accuracy of optimum nutrition intake than Genetic Algorithm (GA), while GA can only provide an optimum nutrition intake accuracy of 97.87%. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Optimum nutrition Energy requirement Energy intake Dietary habit recommendation binary particle swarm optimization

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Feature Selection Using binary particle swarm optimization with Time Varying Inertia Weight Strategies 18

Feature Selection Using Binary Particle Swarm Optimization w...

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2nd International Conference on Future Networks and Distributed Systems (ICFNDS)

作者： Mafarja, Majdi Jarrar, Radi Ahmad, Sobhi Abusnaina, Ahmed A. Birzeit Univ Dept Comp Sci Birzeit Palestine

ISBN: (纸本)9781450364287

In this paper, a feature selection approach that based on binary particle swarm optimization (PSO) with time varying inertia weight strategies is proposed. Feature Selection is an important preprocessing technique that aims to enhance the learning algorithm (e.g., classification) by improving its performance or reducing the processing time or both of them. Searching for the best feature set is a challenging problem in feature selection process, metaheuristics algorithms have proved a good performance in finding the (near) optimal solution for this problem. PSO algorithm is considered a primary swarm Intelligence technique that showed a good performance in solving different optimization problems. A key component that highly affect the performance of PSO is the updating strategy of the inertia weight that controls the balance between exploration and exploitation. This paper studies the effect of different time varying inertia weight updating strategies on the performance of BPSO in tackling feature selection problem. To assess the performance of the proposed approach, 18 standard UCI datasets were used. The proposed approach is compared with well regarded metaheuristics based feature selection approaches, and the results proved the superiority of the proposed approach.

关键词： binary particle swarm optimization PSO Feature Selection Classification Inertia Weight optimization

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Automatic Multilevel Thresholding using binary particle swarm optimization for image segmentation

Automatic Multilevel Thresholding using Binary Particle Swar...

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International Conference of Soft Computing and Pattern Recognition

作者： Djerou, Leila Khelil, Nacer Dehimi, Houssem Eddine Batouche, Mohamed Med Khider Univ Dept Comp Sci Biskra Algeria Med Khider Univ Dept Math Biskra Algeria King Saud Univ CCIS Dept Comp Sci Riyadh Saudi Arabia

ISBN: (纸本)9781424453306

In this paper an automatic multilevel thresholding approach, based on binary particle swarm optimization, is proposed. The proposed approach automatically determines the "optimum" number of the thresholds and simultaneously searches the optimal thresholds, by optimizing a function which uses the gray level thresholds as parameters. The algorithm starts with large number initial thresholds, then, these thresholds are dynamically refined to improve the value of the objective function. The proposed method is validated by illustrative examples;comparison with the exhaustive search Otsu's and Kapur's methods shows its efficiency.

关键词： Automatic Thresholding binary particle swarm optimization Image segmentation Kapur's method Otsu's method

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