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A novel test-cost-sensitive attribute reduction approach using the binary bat algorithm

KNOWLEDGE-BASED SYSTEMS 2019年 186卷 104938-000页

作者： Xie, Xiaojun Qin, Xiaolin Zhou, Qian Zhou, Yanghao Zhang, Tong Janicki, Ryszard Zhao, Wei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China McMaster Univ Dept Comp & Software Hamilton ON L8S 4K1 Canada Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

Attribute reductions are essential pre-processing steps in such as data mining, machine learning, pattern recognition and many other fields. Moreover, test-cost-sensitive attribute reductions are often used when we have to deal with cost-sensitive data. The main result of this paper is a new meta-heuristic optimization method for finding optimal test-cost-sensitive attribute reduction that is based on binary bat algorithm that originally was designed to model the echolocation behavior of bats when they search their prey. First we provide a 0-1 integer programming algorithm that can calculate optimal reduct but is inefficient for large data sets. We will use it to evaluate other algorithms. Next, a new fitness function that utilizes the pairs of inconsistent objects and does not have any uncertain parameter is design and an efficient algorithm for counting inconsistent pairs is provided. Then, an efficient test-cost-sensitive attribute reduction technique that uses binary bat algorithm is provided. Finally, a new evaluation model with four different evaluation metrics has been proposed and used to evaluate algorithms that only provide sub-optimal solutions. Several experiments were carried out on broadly used benchmark data sets and the results have shown the superiority of our new algorithm, in terms of various metrics, computational time, and classification accuracy, especially for high-dimensional data sets. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Rough set Attribute reduction Test-cost-sensitive binary bat algorithm

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Digital Watermarking for Relational Database Security using mRMR based binary bat algorithm 17

Digital Watermarking for Relational Database Security using ...

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17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Tufail, Hina Zafar, Kashif Baig, Rauf Natl Univ Comp & Emerging Sci Lahore Pakistan Al Imam Muhammad Ibn Saud Islamic Univ Riyadh Saudi Arabia

ISBN: (纸本)9781538643877

Publically available relational data without security protection may cause data protection issues. Watermarking facilitates solution for remote sharing of relational database by ensuring data integrity and security. In this research, a reversible watermarking for numerical relational database by using evolutionary technique has been proposed that ensure the integrity of underlying data and robustness of watermark. Moreover, mRMR based feature subset selection technique has been used to select attributes for implementation of watermark instead of watermarking whole database. binary bat algorithm has been used as constraints optimization technique for watermark creation. Experimental results have shown the effectiveness of the proposed technique against data tempering attacks. In case of alteration attacks, almost 70% data has been recovered, 50% in deletion attacks and 100% data is retrieved after insertion attacks. The watermarking based on evolutionary technique (WET) i.e., mRMR based binary bat algorithm ensures the data accuracy and it is resilient against malicious attacks.

关键词： Database security watermarking binary bat algorithm feature subset selection

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Optimal PMU placement for power system observability using Taguchi binary bat algorithm

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MEASUREMENT 2017年 95卷 8-20页

作者： Vedik, Basetti Chandel, Ashwani K. NIT Hamirpur Dept Elect Engn Hamirpur HP India

Phasor measurement units (PMUs) are considered as a promising tool in wide area monitoring, protection, and control of power system networks. In this paper, a novel technique based on Taguchi binary bat algorithm (TBBA) is proposed to determine the optimal number and placement locations of PMUs such that power system is completely observable. The proposed TBBA combines the systematic reasoning ability of the Taguchi method with the traditional binary bat algorithm thereby enhances the initial population, and subsequently, improves the computational efficacy of the solution. The effectiveness of the proposed method is demonstrated on a number of power system test systems under different operating conditions. Results thus obtained are compared with the existing techniques published in the literature. From the results it has been found that the proposed technique not only yields multiple global optimal solutions in single experimental run but also provides solution with lesser computational time. Further, the proposed technique provides similar or better optimal solutions in comparison with the other techniques proposed in the literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： binary bat algorithm Observability Optimal placement Phasor measurement unit Taguchi method

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Optimal Placement of Phasor Measurement Units Using binary bat algorithm 19

Optimal Placement of Phasor Measurement Units Using Binary B...

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19th International Middle East Power Systems Conference (MEPCON)

作者： Tawfik, Amr S. Abdallah, Emtethal N. Youssef, Karim H. Alexandria Univ Fac Engn Dept Elect Engn Alexandria Egypt

ISBN: (纸本)9781538609903

The invention of phasor measurement unit (PMU) is considered to be one of the most important breakthroughs in the field of electrical grids monitoring and control PMUs can be used to obtain accurate voltage and current phasors' measurements. The high cost of installation related to PMU limited its usage and increased the need for optimal allocation of PMUs. A binary bat optimization algorithm is proposed in this paper for solving the optimal PMU placement (OPP) problem to obtain a fully observable system. Simulations are carried on various IEEE bus test systems to ensure the algorithm's effectiveness and feasibility. A comparison is made between the obtained results and the other algorithms' results. The execution time of the proposed algorithm is also compared with other algorithms to illustrate the algorithm's ability to obtain the problem solution with lower execution time and better convergence curve.

关键词： binary bat algorithm Optimal placement Phasor measurement unit Observability

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An Improved Chaotic binary bat algorithm for QoS Multicast Routing

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2016年第4期25卷 1650025-1650025页

作者： Meraihi, Yassine Acheli, Dalila Univ MHamed Bougara Boumerdes Automat Dept Appl Automat Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter beta of the pulse frequency f(i). Second, we use a dynamic formulation to update the parameter alpha of the loudness A(i). The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and binary bat algorithm (BBA).

关键词： bat algorithm binary bat algorithm quality of service (QoS) multicast routing chaotic maps

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Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

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INFORMATION SCIENCES 2015年 294卷 423-438页

作者： Kang, Myeongsu Kim, Jaeyoung Kim, Jong-Myon Univ Ulsan Sch Elect Elect & Comp Engn Ulsan 680749 South Korea

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Acoustic emission binary bat algorithm Dimensionality reduction Incipient low-speed bearing fault diagnosis Multiclass support vector machines Wavelet packet transform

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Unsupervised Feature Selection Using binary bat algorithm 2

Unsupervised Feature Selection Using Binary Bat Algorithm

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2nd International Conference on Electronics and Communication Systems (ICECS)

作者： Rani, A. Sylvia Selva Rajalaxmi, R. R. Kongu Engn Coll Dept Comp Sci & Engn Perundurai India

ISBN: (纸本)9781479972258

Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm.

关键词： Unsupervised feature selection binary bat algorithm K -means

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Improved DV-Hop Localization algorithm Based on Variable Communication Radius for Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期138卷 1703-1725页

作者： Cao, Yuxiao Wu, Xiaobin Xu, Jinbao Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Jiangsu Peoples R China

In the field of wireless sensor networks, node localization is a key research issue. As a classical range-free localization scheme, the localization accuracy of distance vector-hop (DV-Hop) algorithm needs to be further improved. Therefore, an improved DV-Hop location algorithm based on variable communication radius (VCR IDV-Hop) is proposed in this paper. Firstly, by changing the communication radius of the anchor nodes, hop counts between nodes are refined from integer to decimal, which improves the accuracy of estimated distance between nodes. Secondly, the calculation of the average hop size of anchor nodes is abstracted into a combinatorial optimization problem, and the binary bat algorithm (BBA) is applied to solve it. Finally, when the unknown node is located, only part of anchor nodes with smaller hop counts from the unknown node are selected to participate in the localization. Simulation results show that VCR IDV-Hop greatly improves the localization accuracy of nodes compared with other excellent localization algorithms.

关键词： Wireless sensor networks Localization Variable communication radius Combinatorial optimization binary bat algorithm

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Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis

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JOURNAL OF SUPERCOMPUTING 2023年第6期79卷 7014-7036页

作者： Souaidia, Chouaib Thelaidjia, Tawfik Chenikher, Salah Larbi Tebessi Univ Elect Engn Dept LABGET Lab Tebessa Algeria

This paper develops a new architecture for bearing fault diagnosis based on independent vector analysis, feature selection, and extreme learning machines classifiers. The suggested method applied to vibration signals includes the following steps: First, the independent vector analysis is introduced to separate vibration signal components from each other. Second, statistical parameters are extracted from all the obtained sources. Then three binary optimisation algorithms such as binary bat algorithm, binary particle swarm optimisation and binary grey wolf optimisation are employed for feature selection one by one. Finally, three classifiers based on extreme learning, artificial neural networks and random forest are used to perform the classification step. The obtained results show that independent vector analysis followed by feature selection based on binary grey wolf optimisation and classification using an extreme learning machine provides an optimal input vector which contains only five features for each sample and a very small misdiagnosis rate equal to 0.76%. The obtained results also prove that the suggested methodology gives the best classification results and high visibility compared to the other studied approaches.

关键词： Bearing fault diagnosis Independent vector analysis binary bat algorithm binary particle swarm optimisation binary grey wolf Extreme learning machine

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A discrete binary version of bat algorithm for multidimensional knapsack problem

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2014年第2期6卷 140-152页

作者： Sabba, Sara Chikhi, Salim Constantine Univ 2 Fac Engn Sci Dept Comp Sci Constantine 25017 Algeria

The nature has become a main inspiration source of scientists for developing new intelligent systems and techniques. Nature-inspired meta-heuristics is a kind of algorithms that imitate the social behaviour of some biological species. The bat algorithm (BA) is a new bio-inspired algorithm recently introduced by Yang (2010a). It is an optimisation method that is based on the echolocation behaviour of microbats. Firstly, the BA has been proposed for continuous problems. In this paper, we propose a discrete binary bat algorithm (BinBA) for solving the optimisation problems in binary space. The proposed algorithm is based on the sigmoid function used by Kennedy and Eberhart in 1997 for their binary particle swarm optimisation algorithm. The BinBA was tested on hard instances of the multidimensional knapsack problem. The obtained results are very promising compared to other bio-inspired algorithms.

关键词： discrete optimisation problem bio-inspired algorithm bat algorithm binary bat algorithm multidimensional knapsack problem MKP

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