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A novel hybrid bat algorithm for solving continuous optimization problems

APPLIED SOFT COMPUTING 2018年 73卷 67-82页

作者： Liu, Qi Wu, Lei Xiao, Wensheng Wang, Fengde Zhang, Linchuan China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Maritime Inst NTU Singapore 639798 Singapore

The bat algorithm (BA), which is a global optimization method, performs poorly on complex continuous optimization problems due to BA's disadvantages such as the premature convergence problem. In this paper, we propose a novel Hybrid bat algorithm (HBA) to improve the performance of BA. Three modification methods are incorporated into the standard BA to enhance the local search capability and the ability to escape from local optimum traps. The effectiveness and contribution of these three modification methods are analyzed by using classical benchmark functions. Moreover, the performance of HBA is evaluated on the numerical functions from the CEC 2014 test suite and compared with those of wellknown optimization algorithms. The statistical test results indicate that HBA is a significant improvement. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Hybrid algorithm bat algorithm Extremal optimization Continuous optimization problems

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Optimization of DC-link voltage regulator using bat algorithm for proportional resonant controller-based current control of shunt active power filter in distribution network

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第6期30卷 1-15页

作者： Parandhaman, Balamurugan Nataraj, Senthil Kumar Baladhandautham, Chitti Babu Vellore Inst Technol Sch Elect Engn Chennai Campus Chennai Tamil Nadu India Indian Inst Informat Technol Design & Mfg Dept Elect Engn Kancheepuram Tamil Nadu India

In this article, proportional resonant (PR) controller-based pulse width modulation (PWM) current control for three-phase, three-leg SAPF with the optimized dc-link controller is implemented to compensate for current harmonics produced by nonlinear loads. The optimization of the dc-link voltage regulator is implemented using bat algorithm (BA). The stability of the current controller with the proposed system with a mathematical model is evaluated in the time and frequency domain. Instantaneous real and reactive power theory (pq-theory) is applied for reference currents generation. Simulation of the proposed controller is performed in MATLAB/Simulink environment. The controller is implemented in FPGA platform and the results of simulation are verified with the experimental prototype. The optimized dc-link results in the improved performance of SAPF. The new dimension of the PR controller in a grid-connected system for harmonic regulation is implemented and validated with experimental results. The objective of reduced current harmonics with a minimum per phase fundamental current and unity power factor at the point of common coupling is achieved.

关键词： bat algorithm harmonic compensation optimization PI control PR control shunt active power filter

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A Novel Radio Resource Optimization Scheme in Closed Access Femtocell Networks Based on bat algorithm

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IEICE TRANSACTIONS ON COMMUNICATIONS 2019年第4期E102B卷 660-669页

作者： Mustika, I. Wayan Fath, Nifty Sulistyo, Selo Yamamoto, Koji Murata, Hidekazu Univ Gadjah Mada Dept Elect Engn & Informat Technol Jln Grafika 2 Kampus UGM Yogyakarta 55821 Indonesia Univ Budi Luhur Fac Engn South Jakarta 12260 Indonesia Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

Femtocell has been considered as a key promising technology to improve the capacity of a cellular system. However, the femtocells deployed inside a macrocell coverage are potentially suffered from excessive interference. This paper proposes a novel radio resource optimization in closed access femtocell networks based on bat algorithm. bat algorithm is inspired by the behavior of bats in their echolocation process. While the original bat algorithm is designed to solve the complex optimization problem in continuous search space, the proposed modified bat algorithm extends the search optimization in a discrete search space which is suitable for radio resource allocation problem. The simulation results verify the convergence of the proposed optimization scheme to the global optimal solution and reveal that the proposed scheme based on modified bat algorithm facilitates the improvement of the femtocell network capacity.

关键词： heterogeneous network macrocell femtocell resource allocation optimization bat algorithm interference management

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Optimal design of L₁-norm based IIR digital differentiators and integrators using the bat algorithm

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IET SIGNAL PROCESSING 2017年第1期11卷 26-35页

作者： Aggarwal, Apoorva Rawat, Tarun K. Upadhyay, Dharmendra K. Netaji Subhas Inst Technol Sect 3 New Delhi 110078 India

In this study, new infinite impulse response (IIR) digital differentiators of second, third and fourth orders based on optimising the L-1-error fitness function using the bat algorithm (BA) are proposed. The coefficients of numerator and denominator of the differentiators are computed by minimising the L-1-norm of the error fitness function along with imposing the constraint for the location of poles and zeros within the unit circle to ensure minimum phase. The transfer function of the differentiators are inverted and transformed into the digital integrators of the same orders. The results obtained for the solutions by the proposed L-1-based BA (L-1-BA) are superior to the designs using other techniques such as particle swarm optimisation and real-coded genetic algorithm. The designed optimal differentiator and integrator are compared with the existing models and are found to be of high accuracy and flatness in a wide frequency range along with minimum absolute magnitude error. The mean relative error (dB) is obtained as low as -67dB and -73dB for the proposed differentiators and integrators, respectively.

关键词： IIR filters minimisation genetic algorithms transfer functions poles and zeros optimal design L-1-norm minimization IIR digital differentiators infinite impulse response integrators bat algorithm L-1-error fitness function differentiator numerator differentiator denominator error fitness function pole-and-zero location differentiator transfer function particle swarm optimisation real-coded genetic algorithm minimum absolute magnitude error

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Research on multi-energy complementary microgrid scheduling strategy based on improved bat algorithm

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ENERGY REPORTS 2022年 8卷 1258-1272页

作者： Zhou, Wei-Hao Wu, Yu-Xiang Zhao, Yan Xu, Jian Shenyang Inst Engn Grad Dept 18 Puchang Rd Shenyang 110136 Peoples R China Shenyang Inst Engn 18 Puchang Rd Shenyang 110136 Peoples R China Shenyang Inst Engn Sch New Energy 18 Puchang Rd Shenyang 110136 Peoples R China

In order to improve the accuracy of integrated energy microgrid cluster scheduling operation in existing technologies, a scheduling optimization measure based on an improved bat algorithm was proposed in integrated energy system in this study. Firstly, the integrated demand response (IDR) was used to enhance the load-side energy utilization link. Secondly, the front-end data fragments of the integrated energy system are optimized and integrated based on fuzzy clustering and Mahalanobis distance. Finally, the bat algorithm is improved and substituted by increasing the adaptive inertia weight coefficient. In the simulation stage, the operation simulation test of the system was realized by MATLAB. Experiments show that the optimized integration method based on fuzzy clustering and Mahalanobis distance can make the front-end data achieve reliable clustering, and reduce the redundancy by more than 80%. After improving the bat algorithm, not only the MAPE and RMSE error of scheduling prediction can be reduced to 1.52% and 17.45%, but also the total cost of system scheduling operation can be reduced by about 2.5%. This study can improve the accuracy, economy and prediction speed of scheduling prediction, and has a good application prospect in the field of integrated energy scheduling optimization. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Integrated energy Fuzzy clustering Mahalanobis distance Adaptive inertia weight coefficient bat algorithm

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New Modified Controlled bat algorithm for Numerical Optimization Problem

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Computers, Materials & Continua 2022年第2期70卷 2241-2259页

作者： Waqas Haider Bangyal Abdul Hameed Jamil Ahmad Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Joel J.P.C.Rodrigues M.Adil Khan Danda B.Rawat Richard Etengu Department of Computing and Technology Iqra University IslamabadPakistan Computer Science Kohat University of Science and Technology(KUST)KohatPakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Federal University of Piauí(UFPI) Teresina-PLBrazil Instituto de Telecomunicacoes Covilh?6201-001Portugal Department Computer Science and Technology Chang’an UniversityXi’anChina Department of Electrical Engineering and Computer Science Howard UniversityWashingtonDCUSA

bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization *** leverages the echolocation feature of bats produced by imitating the bats’searching *** faces premature convergence due to its local search *** of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search *** this work,we proposed an improved variation of BA by applying torus walk to improve diversity and *** *** Computerized bat algorithm(MCBA)approach has been examined for fifteen well-known benchmark test *** finding of our technique shows promising performance as compared to the standard PSO and standard *** proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of *** results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional *** MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

关键词： bat algorithm MCBA ANN ML Torus walk

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An early warning model for customer churn prediction in telecommunication sector based on improved bat algorithm to optimize ELM

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第7期36卷 3401-3428页

作者： Li, Meixuan Yan, Chun Liu, Wei Liu, Xinhong Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Beijing Inst Petrochem Technol Dept Math & Phys Beijing 102617 Peoples R China

The competition in the telecommunications industry is mainly reflected in the competition for the number of key customers. Therefore, how to effectively prevent the loss of large customers is a problem that many telecommunications companies are very concerned about. This paper establishes a customer churn prediction model based on the improved bat algorithm (IBA) optimized extreme learning machine (ELM), with the purpose of discovering potential churn customers in time and taking measures to retain them in advance. This paper uses the IBA to optimize the initial random weights of the ELM, so as to improve the prediction accuracy of the ELM. To overcome the shortcomings of the bat algorithm (BA) that the convergence speed is too slow in the early stage and difficult to converge in the later stage, this paper introduces the inertia weight into the speed update formula. To improve the population diversity and local search ability of the BA, this paper adds a chaotic local search method to balance the global search ability of the BA in the early stage. To control the search range of the BAs in the later stage, the lane flight formula is introduced into the position update formula to speed up the convergence speed of the algorithm. Then according to the result of function optimization, it can be seen that IBA has a significant improvement in convergence speed and accuracy. Finally, IBA was used to optimize ELM, and a customer churn prediction model was established. The empirical results show that the predictive model proposed in this paper can effectively identify lost customers and lay a foundation for improving the competitiveness of the telecommunications industry in the future.

关键词： bat algorithm customer churn extreme learning machine intelligent optimization

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Application of DOE and metaheuristic bat algorithm for well placement and individual well controls optimization

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2017年 46卷 47-58页

作者： Naderi, Meysam Khamehchi, Ehsan Amirkabir Univ Technol Tehran Polytech Fac Petr Engn Hafez Ave Tehran Iran

The design of an optimal gas field development and production management is a complicating task because of influencing various factors on decision-making process. Typical factors include number and type of wells, well locations, and production constraints, economic factors of capital expenditure, operating costs, gas sale price, and different engineering and geological parameters. The situation is further complicated due to uncertainty associated with the nonlinear problem of field development optimization. In this study, first, design of experiment (DOE) techniques including uniform design (UD) and Box-Behnken design (BBD) are used for performance prediction of water drive gas reservoirs. Next, a new metaheuristic bat inspired algorithm (BA) will be applied in optimization process of objective function. Undiscounted net present value (UNPV) is used as an objective function for the determination of optimal number of wells (N), individual well locations (I and J), production rate (Q(g)), perforation thickness (H-p) and the limit of tubing head pressure (THP) for different average reservoir permeability (K-G) and permeability anisotropy (K-v/K-h). In this regard, part of gas reservoir with an active bottom aquifer based on actual data is simulated in order to predict gas recovery factor and cumulative water production data that are necessary for performing production optimization. The results show that reducing gas production rate increases the UNPV. Reducing production rate increases ultimate recovery factor and decreases cumulative water production. However, considering time value of money, it is favorable to produce with high rates and consequently deplete the reservoir over short time scales in order to compensate the reduction of profit. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Production optimization Uniform design Box-behnken design Undiscounted net present value bat algorithm

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bat algorithm for Curve Parameterization in Data Fitting with Polynomial Bezier Curves

Bat Algorithm for Curve Parameterization in Data Fitting wit...

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International Conference on Cyberworlds (CW)

作者： Iglesias, Andres Galvez, Akemi Collantes, Marta Univ Cantabria ETSI Caminos Canales & Puertos Dept Appl Math & Computat Sci Avda Castros S-N E-39005 Santander Spain Toho Univ Fac Sci Dept Informat Sci Funabashi Chiba Japan

ISBN: (纸本)9781467394031

A very important issue in many applied fields is to construct the fitting curve that approximates a given set of data points optimally in the sense of least-squares. This problem arises in a number of areas, such as computer-aided design and manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. Since the problem consists of minimizing the least squares fitting error, it can be formulated as an optimization problem. Unfortunately, it is also a highly nonlinear, over determined, multivariate continuous optimization problem. As a consequence, classical mathematical methods cannot solve it in its generality. Clearly, there is a need for more general techniques to tackle this issue. A critical step in this process is to obtain a suitable parameterization of the data points. In this context, this paper introduces a new method to obtain an optimal solution for the parameterization problem of the least-squares fitting Bezier curve. Our approach is based on a powerful nature-inspired optimization method called bat algorithm, which has been recently introduced to solve hard continuous optimization problems. In spite of these remarkable features for optimization, the bat algorithm has never been applied in the context of data fitting for geometric modeling or computer graphics. To analyze the performance of this approach, it has been applied to some simple yet illustrative examples of Bezier curves with satisfactory results.

关键词： Nature-inspired techniques bat algorithm data fitting reverse engineering curve parameterization Bezier curve

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Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 11975-11988页

作者： Valarmathi, R. Sheela, T. Sathyabama Inst Sci & Technol Fac CSE Chennai Tamil Nadu India Sri Sairam Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Sri Sairam Engn Coll Dept Informat Technol Chennai Tamil Nadu India

Cloud computing is the new technology offering services to build new application through virtualization. Virtualization improves the usage of resource utilization in cloud environment. Recently research in Task Scheduling problem has received more attention because the customerswant to maximize the utilization of resources in a cheaper way. In this paper an enhanced particle swarm optimization (PSO) algorithm for improving the efficiency in the task scheduling has been proposed. A ranging function and tuning function based PSO (RTPSO) based on data locality is introduced in this paper for solving the inertia weight assignment problem in existing PSO algorithm for task scheduling. The large inertia weight and small inertia weight will assist a global search and local search respectively. In addition, we have combined the RTPSO with bat algorithm (RTPSO-B) to improve the optimization. Cloudsim is used in this paper to simulate the task scheduling in cloud environment. The proposed RTPSO-B based task scheduling is compared with various existing task scheduling algorithms such as GA, ACO, ordinary PSO. Simulation results proved proposed RTPSO-B based task scheduling method reduces makespan, cost and increases the utilization of resources.

关键词： Cloud computing Task scheduling Particle swarm optimization bat algorithm

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