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Critical analysis: bat algorithm-based investigation and application on several domains

WORLD JOURNAL OF ENGINEERING 2021年第4期18卷 606-620页

作者： Umar, Shahla U. Rashid, Tarik A. Univ Kirkuk Dept Comp Sci & Informat Technol Kirkuk Iraq Sulaimani Polytech Univ Sulaimani Tech Coll Informat Krg Iraq Sulaimani Polytech Univ Sulaimani Tech Coll Informat Sulaimani Iraq Univ Kurdistan Hewler Sch Sci & Engn Comp Sci & Engn Dept Hewler Krg Iraq Univ Kurdistan Hewler Sch Sci & Engn Comp Sci & Engn Dept Erbil Iraq

Purpose The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms. Design/methodology/approach bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm). Findings Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

关键词： Swarm intelligence bat algorithm Metaheuristic algorithms Nature-inspired algorithms Optimization algorithms

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BA-BNN: Detect LDoS Attacks in SDN Based on bat algorithm and BP Neural Network 19

BA-BNN: Detect LDoS Attacks in SDN Based on Bat Algorithm an...

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19th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA)

作者： Li, Xinmeng Luo, Nengguang Tang, Dan Zheng, Zhiqing Qin, Zheng Gao, Xinxiang Hunan Univ Coll Comp Sci & Elect Engn CSEE Changsha Hunan Peoples R China

ISBN: (纸本)9781665435741

Low-rate Denial of Service (LDoS) attack is a novel form of the denial-of-service attack. Consequently, there is something difficult to detect it by conventional attack detection techniques. BP neural network detects LDoS attacks is feasible because of its features such as high non-linear. However, it also has the defects such as poor global search ability. bat algorithm that has the strong global search ability can make up those defects. We proposed a LDoS attack detection method that is on account of bat algorithm and BP Neural Network (BA-BNN). For the sake of making a verification on how valid our method is, a series of LDoS attack experiments were carried out in a virtual Software Defined Network (SDN) composed of Mininet emulator and Ryu controller, obtaining the TCP and UDP traffic data. We use the algorithm called random forest to select characteristics of training data for BP neural network ameliorated by an intelligent algorithm, bat algorithm, and construct the LDoS attack detection model. We compare our method with some other methods. The results show that the method we adopt is a highly accurate LDoS attack detection method. It has higher correct rate and detection rate, and lower false negative rate and false positive rate.

关键词： LDoS attack Attack detection SDN BP neural network bat algorithm Characteristic selection

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Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by bat algorithm

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HELIYON 2023年第11期9卷 e21351页

作者： Mekaoussi, Hayat Heddam, Salim Bouslimanni, Nouri Kim, Sungwon Zounemat-Kermani, Mohammad Univ Batna 1 Hadj Lakhdar Inst Vet & Agron Sci Agron Dept Hydraul Div Allees 19 MaiRoute Biskra Batna Batna 05000 Algeria Univ 20 Aout 1955 Skikda Lab Res Biodivers Interact Ecosyst & Biotechnol LR Skikda Algeria Univ 20 Aout 1955 Skikda Fac Sci Agron Dept Lab Res Biodivers Interact Ecosyst & Biotechnol LR Route El HadaikBP 26 Skikda Algeria Univ Batna 1 Hadj Lakhdar Inst Vet & Agron Sci Agron Dept Chem Div Allees 19 MaiRoute Biskra Batna Batna 05000 Algeria Dongyang Univ Dept Railrd Construct & Safety Engn Yeongju 36040 South Korea Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran

Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by bat algorithm (ELM-bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively.

关键词： Modelling WWTP BOD 5 ELM bat algorithm RVFL RFR GPR MLPNN

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An Improved bat algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

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SN Computer Science 2021年第4期2卷 290页

作者： Sahoo, Biswa Mohan Amgoth, Tarachand Department of Computer Science and Engineering Amity University Uttar Pradesh Greater Noida India Indian Institute of Technology (Indian School of Mines) Dhanbad India

Clustering is one of the imperative solutions to the problem of energy unbalancing prevailing in the network. The major issues of wireless sensor network with unequal clustering and multi-hop communication are the energy stability and network longevity. In this paper, we consider the improved bat optimization algorithm to improve the unequal clustering techniques by forming groups before selecting Cluster Heads. In terms of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. bat algorithm (BA), optimization of particle swarm, differential evolution, and whale optimization algorithm is now becoming efficient clustering methods as per the metaheuristic approach. Evaluate the life span of the entire network;this paper proposes an improved BA. The BA’s primary goal of unequal clustering is to reduce energy consumption and extend the lives of the WSNs. An objective function has been formulated to reduce energy consumption and increase the network’s lifespan to achieve these goals. Compared with three existing optimization methods, the experimental results showed that the proposed improved BA achieved better efficiency in reducing total power consumption: whale optimization for topology control, and improved particle swarm optimization algorithm. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bat algorithm Heterogeneous WSNs Meta-heuristic Network performance Unequal clustering

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Improving Convergence Speed of bat algorithm Using Multiple Pulse Emissions along Multiple Directions

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SENSORS 2022年第23期22卷 9513-9513页

作者： Younas, Waqar Ali, Gauhar Ahmad, Naveed Abbas, Qamar Masood, Muhammad Talha Munir, Asim ElAffendi, Mohammed Int Islamic Univ Islamabad Dept Comp Sci & Software Engn Islamabad 44000 Pakistan Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci & Blockchain Lab Riyadh 11586 Saudi Arabia

Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and bat algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others.

关键词： bat algorithm pulse emission function optimization levy flights

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A novel resource management framework in a cloud computing environment using hybrid cat swarm bat (HCSbat) algorithm

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DISTRIBUTED AND PARALLEL DATABASES 2023年第1-2期41卷 53-63页

作者： Senthil Kumar, A. M. Padmanaban, K. Velmurugan, A. K. Asha Shiny, X. S. Anguraj, Dinesh Kumar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India Nalla Malla Reddy Engn Coll Dept Comp Sci & Engn Hyderabad Telangana India

Resource management is an important issue in the cloud computing paradigm. The maximum Utilization of resources leads the service providers to get maximum profit. The suitable resources in the cloud data centre are needed to satisfy the user needs. The selection of the right resource is very essential in the data centre. Metaheuristic algorithms are used to perform the task allocation in the cloud computing environment. In this paper, a novel task allocation algorithm is proposed using Cat Swarm Optimization and bat algorithm. bat algorithm helps CSO algorithm to escape from a pre convergence issue. The proposed HGCSbat algorithm results are evaluated and compared with the popular algorithms CSO and bat algorithms. The HGCSbat results are better than Genetic algorithm, Cat Swarm Optimization, and bat algorithms in terms of availability and throughput.

关键词： Cloud computing Resource management Cat Swarm optimization bat algorithm Availability Throughput

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DESIGN OF STATE FEEDBACK CONTROLLER WITH OPTIMAL PARAMETERS USING bat algorithm FOR REACTION WHEEL PENDULUM

DESIGN OF STATE FEEDBACK CONTROLLER WITH OPTIMAL PARAMETERS ...

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International Conference on Advanced Technologies for Communications (ATC)

作者： Chiem Nguyen Xuan Thang Le Tran Le Quy Don Tech Univ Dept Automat & Comp Tech Hanoi Vietnam Controls Automat Prod & Improvement Technol Inst Hanoi Vietnam

ISBN: (纸本)9781665433792

This paper presents a method of designing a positional stability control system for the Reaction Wheel Pendulum. The Reaction Wheel Pendulum is a single-input, multiple-output (SIMO) nonlinear system. The state feedback controller is designed for this system by choosing the distribution of the solutions of the above characteristic polynomial in the solution space. This original distribution is optimized by the bat algorithm through the ITAE objective function. The results of the synthetic control law are proven through simulation results and comparison with other control laws. The effectiveness of the rule with optimized parameters is also shown in the results on the embedded control system.

关键词： Reaction Wheel Pendulum LQR controller SIMO bat algorithm pole-assignment controller ITAE

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Optimization of User Comfort Index for Ambient Intelligence Using Enhanced bat algorithm 8th

Optimization of User Comfort Index for Ambient Intelligence ...

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8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020

作者： Abdul Malek, Mohamad Razwan Ab. Aziz, Nor Azlina Baharudin, Farah Nur Arina Ibrahim, Zuwairie Faculty of Engineering and Technology Multimedia University Melaka Malaysia College of Engineering Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (纸本)9789811648021

Ambient Intelligence (AmI) is an attribute to the environment that can be controlled and monitored by the smart electronic systems that are responsive to the users’ behavior and cater for their needs. AmI is spreading rapidly to many discipline areas and beneficial to the society. Specifically, AmI is achieved through five technology areas which are sensors, networks, artificial intelligence, human computer interfaces and pervasive ubiquitous computing. One of the objective of AmI is to maximize the user comfort index. In this paper three parameters namely;temperature, illumination and indoor air quality are used to determine the user comfort index. Metaheuristics methods such as bat algorithm (BA) are suitable for the maximization of user comfort index. Three variants of enhanced bat algorithm (EBA) are used here to optimized these parameters while balancing occupant’s preference and environmental aspect. The EBA adopted random, linear and nonlinear inertia weight. The findings show that the performance of the EBA is better than the original BA in optimizing the user comfort index. Specifically, the EBA with nonlinear inertia weight provides the best average user comfort index for the set of experimental data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ambient intelligence bat algorithm User comfort index

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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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Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight

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JOURNAL OF BUILDING ENGINEERING 2022年 47卷

作者： Malek, Mohamad Razwan Abdul Ab Aziz, Nor Azlina Alelyani, Salem Mohana, Mohamed Baharudin, Farah Nur Arina Ibrahim, Zuwairie Multimedia Univ Fac Engn & Technol Melaka 75450 Malaysia King Khalid Univ CAI Abha 61421 Saudi Arabia King Khalid Univ Coll Comp Sci Abha 61421 Saudi Arabia Univ Malaysia Pahang Coll Engn Kuantan 26300 Pahang India

Smart home is a concept that aims to maximize the comfort of occupant while consuming energy as low as possible. The comfort and energy consumption are contradicting factors in smart homes. Enhancing comfort often requires considerable energy. On the other hand, minimizing energy may result in less comfort to the residence. Thus, maximizing comfort while minimizing energy consumption is a challenging process. In this paper, bat algorithm (BA) based solution is proposed to tackle this problem. Three main parameters that influence the occupant's comfort, namely, temperature, illumination, and indoor air quality, are considered. The algorithm optimizes towards the best set of values for the three parameters. In this work, exponentially increasing inertia weight is introduced to BA for performance improvement. A secondary dataset consisting of 48 environmental values is optimized using the proposed algorithm. The performance of BA with exponential inertia weight is proven as significantly better than the original BA and other variants of inertia weight;random, linearly decreasing, and nonlinearly decreasing. Moreover, the comfort level achieved by BA with exponential inertia weight is found to be better than previously reported works using firefly algorithm, genetic algorithm, ant colony optimization, and artificial bee colony algorithm. The superior performance is achieved due to better convergence behaviour. Summarily, applying BA with exponential inertia weight is a novel contribution that is essential for providing smart home system a solution that ensures optimum comfort at minimal energy consumption.

关键词： bat algorithm Comfort Inertia weight Energy consumption Optimization

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