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A Novel Hybrid binary bat algorithm for Global Optimization

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2024年第1期15卷 1页

作者： Liang, Huijun Pang, Aokang Lin, Chenhao Zhong, Jianwei Hubei Minzu Univ Coll Intelligent Syst Sci & Engn Enshi Peoples R China Hubei Minzu Univ Enshi Peoples R China

In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time -varying v -shaped transfer function, rather than a time -invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

关键词： binary bat algorithm Random Black Hole Model Transfer Function Composition Benchmark Function Unit Commitment

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Fetal state health monitoring using novel Enhanced binary bat algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Sharma, Prerna Sharma, Kapil Delhi Technol Univ Delhi India Delhi Technol Univ Informat Technol Delhi India

The timely assessment of hypoxic fetuses by cardiotocography is of utmost significance as deficiency of oxygen in fetuses' leads to fetal distress which can further prove to be fatal or cause neurological diseases. This paper puts forward an Enhanced binary bat algorithm, the altered form of binary bat algorithm for the multi-classification problem of Cardiotocography. Subset of optimal and relevant features is selected using the optimized and Enhanced BBA algorithm from cardiotocography dataset. Features selected by various evolutionary algorithms EBBA (Enhanced binary bat algorithm), qGWO (quantum Grey Wolf Optimization), Genetic algorithm are 11, 15, 12 simultaneously. EBBA efficiently selects most reduced set of features. The proposed EBBA can be used in feature selection and classification of cardiotocography dataset under different fetus state i.e. normal, suspect and pathologic with an accuracy of 96.21% using machine learning classifier i.e. random forest.

关键词： Cardiotocography Classification Feature selection Evolutionary algorithm bat algorithm binary bat algorithm Fetal distress

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A wrapper based binary bat algorithm with greedy crossover for attribute selection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 187卷 115828-115828页

作者： Akila, S. Christe, S. Allin PSG Coll Technol Dept Elect & Commun Engn Coimbatore 641004 Tamil Nadu India

Attribute selection plays a vital role in optimization and machine learning that involves huge datasets. Classification accuracy of any learning model depends on the dimensionality of data and attributes selected. This leads to a multi-objective problem of obtaining high classification accuracy with fewer attributes. In this research work, a multi-objective optimization algorithm with greedy crossover for attribute selection and classification is proposed. A wrapper based binary bat algorithm (BBA) with Support Vector Machine (SVM) as evaluator is implemented for attribute selection. In general, the optimization algorithms have the tendency to prematurely converge with sub-optimal solutions. This reduces the quality of the attribute selected and efficiency of the algorithm. Here, a multi-objective binary bat algorithm with greedy crossover is proposed to reset the sub-optimal solutions that are obtained due to the premature convergence. The evaluation of the attributes selected is done using the Support Vector Machine with 10-fold cross-validation. The proposed algorithm is implemented and evaluated with the benchmark datasets available in the UC Irvine (UCI) repository. Classification accuracy of 89.25%, 96.45%, 96.57% and 88.50% using the Australian, Ionosphere, Wisconsin Breast Cancer (Original dataset) and Musk is obtained. Further analysis is made with parameter metrics like sensitivity, specificity, precision, recall, fmeasure, Matthews Correlation coefficient (MCC), confusion matrix and Area under the ROC Curve (AUC). The proposed multi-objective binary bat algorithm with greedy crossover yields better performance over the existing bat based algorithms and other nature-inspired algorithms. The solution for the multiobjective problem of obtaining high classification accuracy with minimal number of attributes is attained. Also, the problem of premature convergence occurring in the optimization algorithms with sub-optimal solutions is overcome using the proposed

关键词： Attribute selection Classification Nature-inspired algorithm SVM binary bat algorithm Wrapper based algorithms

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A novel quantum-inspired binary bat algorithm for leukocytes classification in blood smear

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EXPERT SYSTEMS 2022年第3期39卷

作者： Sharma, Prerna Sharma, Kapil Delhi Technol Univ Comp Sci & Engn Delhi India Delhi Technol Univ Informat Technol Delhi India

In the field of medical sciences, day-to-day procedure is followed for identification of bone marrow and immune system related diseases, which is most of the time carried out manually. The notion is to perform differential and qualitative analysis of leukocytes for the timely diagnosis of these diseases. In this article, a systematized solution is offered for the classification of leukocytes in blood smear. The proposed model incorporates the optimistic aspects of nature-inspired and quantum-inspired algorithms;this model tends to be perfect blend of both the techniques. For reducing the dimensionality, that is, irrelevant features;the quantum-inspired binary bat algorithm (QBBA) has been used in the proposed model. The optimality of features selected has been computed with the help of accuracy measure using various machine learning classifiers like Logistic Regression, KNN, Random Forest, Decision Tree. The performance of QBBA and its customary algorithms has been compared and the results depict that QBBA outperforms binary bat algorithm for the same set of population. QBBA comes out as an influential algorithm with an average accuracy of 98.31% and also possess enhanced noise invulnerability. The proposed QBBA can also find its usage in thorough haematological analysis.

关键词： binary bat algorithm classification evolutionary algorithm feature selection leukocytes quantum-inspired binary bat algorithm quantum-inspired computing

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Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第5期34卷

作者： Seetharaman, Akila Sundersingh, Allin Christe PSG Coll Technol Dept Elect & Commun Engn Coimbatore 641004 Tamil Nadu India

DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly predictive gene features must be obtained without affecting the accuracy. Previous studies concentrated either on improving the classification accuracy or reduction of gene features. Here, the multi-objective problem of obtaining reduced gene features with high classification accuracy is addressed using the proposed correlation feature selection filter and binary bat algorithm (BBA) with greedy crossover. The gene feature subsets are obtained using the correlation based feature selection filter and optimized using the BBA. Suboptimal solutions obtained due to pre-convergence of BBA are reset using the proposed greedy crossover. Highly predictive genes features are obtained and evaluated with support vector machine 10-fold cross-validation. An average classification accuracy of 95.85% with predictive gene features <1% of the total dataset was obtained when applied on cancer microarray datasets. The solution for the multi-objective problem of obtaining high classification accuracy with minimal number of genes is achieved with better performance over the existing algorithms. Also, the problem of pre-convergence with suboptimal solutions in optimization algorithms is overcome.

关键词： binary bat algorithm cancer classification gene selection microarray classification nature inspired optimization algorithm SVM

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Feature Selection for Image Steganalysis Using binary bat algorithm

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IEEE ACCESS 2020年 8卷 4244-4249页

作者： Liu, Feng Yan, Xuehu Lu, Yuliang Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China

Steganography is to hide secret information in a normal cover, so that the secret information cannot be detected. With the rapid development of steganography, it's more and more difficult to detect. Steganalysis is the counter of steganography. In order to improve the detection effect, more complex high-dimensional features are proposed for steganalysis. However, this also creates huge redundancy features, which in turn consume generous time. Feature selection is a technique that can effectively remove redundant features. In this paper, we propose a new blind image steganalysis algorithm to distinguish stego images from cover images using a nature-inspired feature selection method based on the binary bat algorithm(BBA). Meanwhile, SPAM and several classifiers have been used to improve the detection effect. Furthermore, we select the ideal feature subset using BBA from the original features and use the selected feature subset to train the several classifiers. The experimental results demonstrate that our proposed method can improve the detection effect and reduces the redundant features.

关键词： Image steganalysis feature selection binary bat algorithm swarm intelligence

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Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the binary bat algorithm

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ENERGIES 2021年第1期14卷 255页

作者： Sipos, Mario Klaic, Zvonimir Nyarko, Emmanuel Karlo Fekete, Kresimir Croatian Mil Acad Dr Franjo Tudjman Zagreb 10000 Croatia Josip Juraj Strossmayer Univ Osijek Fac Elect Engn Comp Sci & Informat Technol Osijek Osijek 31000 Croatia

Voltage dips represent a significant power quality problem. The main cause of voltage dips and short-term interruptions is an electrical short circuit that occurs in transmission or distribution networks. Faults in the power system are stochastic by nature and the main cause of voltage dips. As faults in the transmission system can affect more customers than faults in the distribution system, to reduce the number of dips, it is not enough to invest in a small part of the transmission or distribution system. Only targeted investment in the whole (or a large part of the) power system will reduce voltage dips. Therefore, monitoring parts of the power system is very important. The ideal solution would be to cover the entire system so that a power quality (PQ) monitor is installed on each bus, but this method is not economically justified. This paper presents an advanced method for determining the optimal location and the optimal number of voltage dip measuring devices. The proposed algorithm uses a monitor reach area matrix created by short-circuit simulations, and the coefficient of the exposed area. Single-phase and three-phase short circuits are simulated in DIgSILENT software on the IEEE 39 bus test system, using international standard IEC 60909. After determining the monitor reach area matrix of all potential monitor positions, the binary bat algorithm with a coefficient of the exposed area of the system bus is used to minimize the proposed objective function, i.e., to determine the optimal location and number of measuring devices. Performance of the binary bat algorithm is compared to the mixed-integer linear programming algorithm solved by using the GNU Linear Programming Kit (GLPK).

关键词： voltage dip PQ monitor monitor reach area matrix binary bat algorithm

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QoS multicast routing for wireless mesh network based on a modified binary bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 3057-3073页

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

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm.

关键词： bat algorithm binary bat algorithm Quality of service (QoS) Multicast routing Multicast tree

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Optimized binary bat algorithm for classification of white blood cells

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MEASUREMENT 2019年 143卷 180-190页

作者： Gupta, Deepak Arora, Jatin Agrawal, Utkarsh Khanna, Ashish de Albuquerque, Victor Hugo C. Maharaja Agrasen Inst Technol Delhi India Univ Fortaleza Grad Program Appl Informat Fortaleza Ceara Brazil

The quantitative and differential analysis of leukocytes present in human body provide conducive hematological information to physicians for diagnosis of various infections and ailments. This paper proposes an Optimized binary bat algorithm, an enhanced version of the original binary bat algorithm, for classification of different types of leukocytes. It is used for the first time in this field of application to the best of our knowledge. A set of features are extracted from images of WBCs and then the optimized algorithm is used to obtain a subset of those features which are essential and more relevant from the high-dimensional dataset. Similar to the original BBA, the optimized BBA is an evolutionary algorithm inspired by the echolocation technique used by bats for locating a prey or an object. OBBA aims to reduce the dimensionality of the dataset by determining the features which are most discriminative. The proposed algorithm is implemented using four different classifiers, K-nearest neighbors (KNN), Logistic Regression, Random Forest and Decision Tree, and their performance is compared. The proposed OBBA can be used in classification of WBCs with an average accuracy of 97.3% and help in analysis of numerous hematological conditions. The optimized BBA is also compared with other nature inspired algorithms including Optimized Crow Search algorithm and Optimized Cuttlefish algorithm, and the results indicate that the suggested algorithm is sufficiently fast and accurate to be used in hematological analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： White blood cell Classification Feature selection BBA Optimised BBA binary bat algorithm Evolutionary algorithm Nature inspired Optimization

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Optimal Transmission System Expansion Planning Via binary bat algorithm 21

Optimal Transmission System Expansion Planning Via Binary Ba...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abou El-Ela, Adel Mouwafi, Mohamed Al-Zahar, Walid Menoufia Univ Elect Engn Dept Fac Engn Menoufia Egypt Egyptian Elect Transmiss Co West Delta Reg OHTL Cables Sect Cairo Egypt

ISBN: (纸本)9781728152899

This paper presents an approach procedure based on binary bat algorithm (BBA) for solving the transmission system expansion planning (TSEP) problem. The proposed BBA is applied to achieve the comprehensive objective function with the lowest investment and operation costs by finding the optimal distributions of new transmission lines at different operating conditions. The suggested operating conditions are the normal and the contingency such as any single line outage (one by one). The AC optimal power flow (AC-OPF) using MATPOWER is utilized to find the OPF calculations. The proposed BBA is tested on Garver's 6-bus test system and the west Delta system (WDS) 21-bus as a part of the Egyptian electricity transmission network (EETN) to solve the TSEP problem and the results are compared with other techniques to prove the robustness of the proposed procedure for solving the TSEP problem considering different technical and economic benefits.

关键词： transmission system expansion planning binary bat algorithm normal operating condition contingency condition

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