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bat algorithm with Weibull walk for solving global optimisation and classification problems

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第3期15卷 159-170页

作者： Rauf, Hafiz Tayyab Hadi, Muhammad Rehman, Abdur Univ Gujrat Dept Comp Sci Gujrat Pakistan

bat algorithm (BA) becomes the most widely employed meta-heuristic algorithm to interpret the diverse kind of optimisation and real-world classification problems. BA suffers from one of the influential challenges called local minima. In this study, we carry out two modifications in the original BA and proposed a modified variant of BA called bat algorithm with Weibull walk (WW-BA) to solve the premature convergence issue. The first modification involves the introduction of Weibull descending inertia weight for updating the velocity of bats. The second modification approach updates the local search strategy of BA by replacing the Random walk with the Weibull Walk. The simulation performed on 19 standard benchmark functions represent the competence and effectiveness of WW-BA compared to the state of the art techniques. The proposed WWBA is also examined for classification problem. The empirical results reveal that the proposed technique outperformed the classical techniques.

关键词： bat algorithm premature convergence exploration exploitation Weibull walk inertia weight

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bat algorithm-based beamforming for mmWave massive MIMO systems

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第2期33卷 e4182-e4182页

作者： Shahjehan, Waleed Riaz, Asad Khan, Imran Sadiq, Ali Safaa Khan, Suleman Khan, Muhammad Khurram Univ Engn & Technol Dept Elect Engn Peshawar Pakistan Univ Wolverhampton Sch Math & Comp Sci Wolverhampton Cyber Res Inst Wulfruna St Wolverhampton WV1 1LY W Midlands England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England King Saud Univ Ctr Excellence Informat Assurance Riyadh Saudi Arabia

In this paper, an optimized analog beamforming scheme for millimeter-wave (mmWave) massive MIMO system is presented. This scheme aims to achieve the near-optimal *** searching for the optimized combination of analog precoder and combiner. In order to compensate for the occurrence of attenuation in the magnitude of mmWave signals, the codebook-dependent analog beamforming in conjunction with precoding at transmitting end and combining signals at the receiving end is utilized. Nonetheless, the existing and traditional beamforming schemes involve a more difficult and complicated search for the optimal combination of analog precoder/combiner matrices from predefined codebooks. To solve this problem, we have referred to a modified bat algorithm to find the optimal combination value. This algorithm will explore the possible pairs of analog precoder/combiner as a way to come up with the best match in order to attain near-optimal performance. The analysis shows that the optimized beamforming scheme presented in this paper can improve the performance that is very close to the beam steering benchmark that we have considered.

关键词： bat algorithm beamforming optimization massive MIMO millimeter-wave

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An Improved bat algorithm Using Density-Based Clustering

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INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE 2023年第72期26卷 102-123页

作者： Al-Asadi, Samraa Adnan Al-Mamory, Safaa O. Univ Babylon Coll Informat Technol Dept Informat Networks Hillah Iraq Univ Informat Technol & Commun Coll Business Informat Baghdad Iraq

bat algorithm is a nature-inspired metaheuristic algorithm that depends on the principle of the echolocation behavior of bats. However, due to poor exploration, the algorithm suffers from being stuck in the local optima. An improved bat algorithm based on the density-based clustering technique is proposed to enhance the algorithm's performance. In this paper, the initial population is improved by generating two populations, randomly and depending on the clusters' center information, and by getting the fittest individuals from these two populations, the initial improved one is generated. The random walk function is improved using chaotic maps instead of the fixed-size movement, so the local search is improved, as well as the global search abilities, by diversifying the solutions. Another improvement is dealing with stagnation by partitioning the search space into two parts depending on the generated clusters' information to obtain the newly generated solution, comparing their quality with the previously generated solution, and choosing the best. The performance of the proposed improved bat algorithm is evaluated by comparing it with the original bat algorithm over ten benchmark optimization test functions. Depending on the results, the improved bat outperforms the original bat by obtaining the optimal global solutions for most of the benchmark test functions.

关键词： Metaheuristic algorithms bat algorithm Density-based Clustering Chaotic Strategies

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Classification Model for Diabetes Mellitus Diagnosis based on K-Means Clustering algorithm Optimized with bat algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 653-659页

作者： Anam, Syaiful Fitriah, Zuraidah Hidayat, Noor Maulana, Mochamad Hakim Akbar Assidiq Brawijaya Univ Math Dept Malang Indonesia Brawijaya Univ Math Dept Malang Indonesia

Diabetes mellitus is a disease characterized by abnormal glucose homeostasis resulting in an increase in blood sugar. According to data from the International Diabetes Federation (IDF), Indonesia ranks 7th out of 10 countries with the highest number of diabetes mellitus patients in the world. The prevalence of patients with diabetes mellitus in Indonesia reaches 11.3 percent or there are 10.7 million sufferers in 2019. Prevention, risk analysis and early diagnosis of diabetes mellitus are necessary to reduce the impact of diabetes mellitus and its complications. The clustering algorithm is one of methods that can be used to diagnose and analyze the risk of diabetes mellitus. The K-mean Clustering algorithm is the most commonly used clustering algorithm because it is easy to implement and run, computation time is fast and easy to adapt. However, this method often gets to be stuck at the local optima. The problem of the K -means Clustering algorithm can be solved by combining the K -means Clustering algorithm with the global optimization algorithm. This algorithm has the ability to find the global optimum from many local optimums, does not require derivatives, is robust, easy to implement. The bat algorithm (BA) is one of global optimization methods in swarm intelligence class. BA uses automated enlargement techniques into a solution and it's accompanied by a shift from exploration mode to local intensive exploitation. Based on the background that has been explained, this article proposes the development of a classification model for diagnosing diabetes mellitus based on the K-means clustering algorithm optimized with BA. The experimental results show that the K-means clustering optimized by BA has better performance than K-means clustering in all metrics evaluations, but the computational time of the K-means clustering optimized by BA is higher than K-means clustering.

关键词： -Diabetes mellitus disease diagnosis methods k-means clustering algorithm optimization bat algorithm

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Multi-objective Task Scheduling Optimization Based on Improved bat algorithm in Cloud Computing Environment

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 1091-1100页

作者： Yu, Dakun Xu, Zhongwei Mei, Meng Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China

In cloud computing environments, task completion time and virtual machine load balance are two critical issues that need to be addressed. To solve these problems, this paper proposes a Multi-objective Optimization Mutate Discrete bat algorithm (MOMDBA) that improves upon the traditional bat algorithm (BA). The MOMDBA algorithm introduces a mutation factor and mutation inertia weight during the global optimization process to enhance the algorithm's global search ability and convergence speed. Additionally, the local optimization logic is optimized according to the characteristics of cloud computing task scenarios to improve the degree of load balancing of virtual machines. Simulation experiments were conducted using CloudSim to evaluate the algorithm's performance, and the results were compared with other scheduling algorithms. The results of our experiments indicate that when the cost difference between algorithms is within 4.47%, MOMDBA can significantly outperform other methods. Specifically, compared to PSO, GA, and LBACO, our algorithm reduces makespan by 56.26%, 59.87%, and 25.26%, respectively, while also increasing the degree of load balancing by 93.87%, 75.92%, and 39.13%, respectively. These findings demonstrate the superior performance of MOMDBA in optimizing task scheduling and load balancing.

关键词： Cloud computing task scheduling optimization bat algorithm meta-heuristics

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Electromagnetic Detection of Dielectric Cylinders by the bat algorithm

Electromagnetic Detection of Dielectric Cylinders by the Bat...

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IEEE Electrical Design of Advanced Packaging and Systems (EDAPS)

作者： Zhang, Xin Yang, Chunxia Tong, Meisong Shanghai Normal Univ Dept Commun Engn Shanghai Peoples R China Tongji Univ Dept Elect Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781665466134

Due to the ill-posedness of the governing equation, the electromagnetic inversion problem is generally solved by iterative optimization of the objective function which is established by adding regularization terms. The traditional deterministic optimization methods completely rely on the gradient information of the objective function, and there would be a great risk of falling into local extrema in the case of unsatisfactory imaging environment and insufficient diversity of measurement data. Swarm intelligence optimization algorithms introduce random factors, which can help to get rid of local optima. The bat algorithm (BA) is a recently proposed swarm intelligence algorithm that imitates the bat foraging behavior. It has two update modes include global search and local search, and the individual only moves in a direction better than the current position. In this paper, a customized electromagnetic inversion algorithm based on the standard BA is proposed. The position, contour and permittivity of two-dimensional cylindrical section are reconstructed successfully in the numerical simulation.

关键词： electromagnetic inversion bat algorithm swarm intelligence algorithm

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An Intelligent bat algorithm for Web Service Selection with QoS Uncertainty

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BIG DATA AND COGNITIVE COMPUTING 2023年第3期7卷 140页

作者： Etchiali, Abdelhak Hadjila, Fethallah Bekkouche, Amina Univ Tlemcen Comp Sci Dept Tilimsen 13000 Algeria

Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user's request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed "fuzzy Pareto dominance" and "Zero-order stochastic dominance", are highly effective compared to the other heuristics and most of the existing state-of-the-art methods.

关键词： web service selection QoS uncertainty bat algorithm service-oriented computing

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

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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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