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Multiobjective Design Optimization of Stator for Synchronous Generator Using bat algorithm and Analysis of Magnetic Flux Density Distribution

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2021年第9-10期49卷 919-929页

作者： Karaoglan, Aslan Deniz Perin, Deniz Yilmaz, Kemal Balikesir Univ Dept Ind Engn TR-10145 Balikesir Turkey ISBIR Elect Co Dept R&D Balikesir Turkey

In this study, we aimed to optimize 3000 kVA synchronous generator (SG) stator design to obtain the desired magnetic flux density distribution and efficiency. We used Maxwell simulations for experiments on some design parameters of stator (slot height and teeth width). Then second-order regression models are calculated that represent the relations between the factors (design parameters) and the measured performance criteria (called as the responses: stator-teeth flux density, stator-yoke flux density, and efficiency). These regression models are used at the multiobjective optimization phase. bat algorithm (BA) is used for performing the multiobjective optimization. By combining Maxwell with regression modeling and BA, the efficiency of the SG is increased to 96.84% from 96.5% with a more acceptable magnetic flux density (between 1.65 and 1.70 T ranges). The stator-teeth flux density and statoryoke flux density are calculated as 1.9 T and 2.07 T for the current SG, whereas these values are reduced to 1.647 and 1,634 T, respectively, for the optimized SG. Results of this study show how the numerical simulation can be successfully combined with the BA to improve the efficiency of the SG by providing the desired magnetic flux density distribution.

关键词： synchronous generator stator design Maxwell simulation regression modeling nature inspired algorithms swarm-based meta-heuristic bat algorithm multiobjective optimization magnetic flux density distributions efficiency

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Cultural bat Optimization algorithm: An Improved Variant of bat algorithm 4

Cultural Bat Optimization Algorithm: An Improved Variant of ...

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4th IEEE-Nigeria-Section International Conference on Disruptive Technologies for Sustainable Development (IEEE NIGERCON)

作者： Amalo, K. A. Birninkudu, S., I Bukata, B. B. Tech Kaduna Polytech Dept Educ Kaduna Kaduna State Nigeria Bayero Univ Dept Elect Engn Kano Kano State Nigeria

ISBN: (纸本)9781665479783

This paper presents a novel variant of the bat algorithm called the Cultural bat algorithm CAbat. The new variants were achieved using situational and normative knowledge inherent in the cultural algorithm. The idea of the bat algorithm is inspired by the intelligent echolocation behaviors of microbats. These intelligent behaviors are codified as an optimization agent for solving global optimization problems. Although this algorithm has been widely accepted due to its robustness, simplicity, and flexibility, it still suffers from an imbalance between exploration and exploitation. This research developed improved variants of the original bat algorithm using cultural evolution processes to address these challenges Four new variants of the bat algorithm were eventually developed using cultural situational and normative knowledge. The performance of the new variants was tested against the standard bat algorithm using eleven commonly used benchmark functions. An extensive comparative study was conducted using statistical metrics computed over thirty independent runs. Results showed that all the CAbat variants performed better than the original bat algorithm.

关键词： bat algorithm Cultural algorithm Benchmark Functions Global Optimization Cultural bat algorithm

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Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第10期80卷 15377-15390页

作者： Gobi, R. Christ Univ Bangalore Karnataka India

In recent days, accurate localization becomes essential for enabling smartphone-based navigation to attain maximum accuracy in the construction of the real ***-based localization is the widespread solution to achieve and assure effective performance. In this study, a new fingerprint-based localization model using a bat algorithm (BA) is presented stimulated by the echolocation nature of microbats. The presented model adapts BA for estimating the location information. Initially, the presented model applies a Bayesian-rule based objective function. Then, the BA is used for improving the accuracy and analyzing the effects of the initial position of the bats on the localization outcome. For mitigating the estimation error, the Kalman filter is employed for updating the initially determined position using the BA for tracking purposes. The experimental analysis indicated an improvement in real-time performance and decrease in computation complexity. The presented model also obtained maximum localization accuracy with minimum localization error over the compared methods.

关键词： Localization Kalman filter bat algorithm Fingerprint

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M-Type and CD-Type Carrier Based PWM Methods and bat algorithm-Based SHE and SHM for Compact Nine-Level Switched Capacitor Inverter

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IEEE ACCESS 2021年 9卷 87731-87748页

作者： Khan, Shahbaz Ahmad Upadhyay, Deepak Ali, Mohammad Tariq, Mohd Sarwar, Adil Chakrabortty, Ripon K. Ryan, Michael J. Alamri, Basem Alahmadi, Ahmad Aligarh Muslim Univ Zakir Husain Coll Engn & Technol Dept Elect Engn Aligarh 202002 Uttar Pradesh India Univ New South Wales Canberra ADFA Sch Engn & Informat Technol Capabil Syst Ctr Canberra BC 2612 Australia Taif Univ Coll Engn Dept Elect Engn At Taif 21944 Saudi Arabia

This paper presents new and efficient modulation techniques applied on the recently introduced compact nine-level switched-capacitor inverter (C9LSCI). The paper also discusses the performance of the inverter under selective harmonic elimination (SHE) and mitigation (SHM) based on the heuristic bat-algorithm (BA) technique. Two new modulation techniques employing CD-type carrier waves and M-type carrier waves are proposed, showing a rise in efficiency, an increase in RMS voltage, reduction in capacitor voltage ripple, and a reduction in harmonic distortion (THD). Four recently introduced modulating signals, namely the third harmonic injection, thirty-degree bus clamped PWM (THTDBCPWM), third harmonic injection sixty-degree bus clamped PWM (THSDBCPWM), one pole clamped PWM (OPCPWM). Loss balancing two-pole clamped PWM (LBTPCPWM) are applied to the inverter with the proposed carriers, and a comparative analysis is carried out on different performance parameters. The proposed carriers' efficacy in Alternate Phase Opposition Disposition (APOD), Phase Opposition Disposition (POD), and Phase Disposition (PD) is validated. Further, bat algorithm (BA) is applied as a heuristic approach to determine the optimum angles for the application of SHE and SHM. The efficacy of all the above techniques on the C9LSCI is verified in MATLAB/Simulink environment and further validated on an experimental setup.

关键词： Pulse width modulation Inverters Harmonic analysis Modulation Capacitors Clamps Topology Sine PWM M-carrier CD-carrier SHE SHM bat algorithm switched capacitor inverter

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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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A Cauchy-Gaussian Quantum-Behaved bat algorithm Applied to Solve the Economic Load Dispatch Problem

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IEEE ACCESS 2021年 9卷 3207-3228页

作者： Rugema, Francois Xavier Yan, Gangui Mugemanyi, Sylvere Jia, Qi Zhang, Shanfeng Bananeza, Christophe Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China Northeast Elect Power Univ Coll Informat Engn Jilin 132012 Jilin Peoples R China Integrated Polytech Reg Coll Tumba Rulindo Rwanda

In this paper, a novel Cauchy-Gaussian quantum-behaved bat algorithm (CGQBA) is applied to solve the economic load dispatch (ELD) problem. The bat algorithm (BA) is an acknowledged metaheuristic optimization algorithm owing to its performance. However, the classical BA presents some weaknesses, such as premature convergence. To withstand the drawbacks of the BA, quantum mechanics theories and Gaussian and Cauchy operators are integrated into the standard BA to enhance its effectiveness. Since the economic load dispatch is a nonlinear, complex and constrained optimization problem, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. The validity of the CGQBA is tested on six standard benchmark functions with different characteristics. The numerical results indicate that the CGQBA is effective and superior to many other algorithms. Moreover, the CGQBA is applied to solve the ELD problems on various test systems including 3,6,20, 40,110 and 160 implemented generating units. The simulation results illustrate the strength of the CGQBA compared with other algorithms recently reported in the literature.

关键词： Optimization Genetic algorithms Economics Convergence Search problems Quadratic programming Statistics Economic load dispatch bat algorithm Gaussian operator Cauchy operator quantum behavior

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A Self-Adaptive Hybrid bat algorithm for Training Feedforward Neural Networks

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2021年第3期12卷 149-171页

作者： Bousmaha, Rabab Hamou, Reda Mohamed Amine, Abdelmalek Univ Saida Dept Comp Sci GeCoDe Lab Saida Algeria

Training feedforward neural network (FFNN) is a complex task in the supervised learning field. An FFNN trainer aims to find the best set of weights that minimizes classification error. This paper presents a new training method based on hybrid bat optimization with self-adaptive differential evolution to train the feedforward neural networks. The hybrid training algorithm combines bat and the self-adaptive differential evolution algorithm called bat-SDE. bat-SDE is used to better search in the solution space, which proves its effectiveness in large space solutions. The performance of the proposed approach was compared with eight evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using seven bio-medical datasets and one large credit card fraud detection dataset. The results of the comparative study show that bat-SDE outperformed other training methods in most datasets and can be an alternative to other training methods.

关键词： bat algorithm Real-World Datasets Self-Adaptive Differential Evolution Training Feedforward Neural Networks

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Improved bat algorithm Based on Fast Diving Strategy

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JOURNAL OF INTERNET TECHNOLOGY 2023年第6期24卷 1249-1262页

作者： Geng, Yanxiang Zhang, Liyi Zhang, Yong Li, Zhixing Li, Jiahui Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

bat algorithm has good global search ability, but it has some problems, such as slow convergence speed in local search stage, low convergence accuracy, easy to fall into local optimization and can not escape. In view of the above defects, inspired by Harris Hawks's strategy of catching rabbits, this paper introduces the surrounding mechanism of prey, which can quickly approach the food and judge its quality, so as to achieve the purpose of rapid convergence and improve the convergence accuracy. The experiment shows that the improved algorithm of the fast diving strategy is tested by using the test function, and compared with the basic bat algorithm, backtracking bat algorithm and HABC. The improved bat algorithm of the fast diving strategy has better optimization accuracy, faster convergence speed, simple algorithm and higher success rate.

关键词： bat algorithm Fast diving strategy Encirclement mechanism Escape CEC06-2019

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A new bat algorithm based on a novel topology and its convergence

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 66卷

作者： Wang, Chunfeng Song, Wenxin Shen, Peiping Xianyang Normal Univ Sch Math & Stat Xianyang 712000 Peoples R China Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Peoples R China North China Univ Water Resources & Elect Power Sch Math & Stat Zhengzhou 450045 Peoples R China

The basic bat algorithm (BA) is a swarm intelligent algorithm, which simulates the echolocation behavior of micro bats. Although BA is easy to implement and efficient in solving some continuous and discrete problems, it has some shortcomings such as premature convergence and low precision. To overcome these shortcomings, this paper presents a new bat algorithm (denoted as CABA) based on a novel topology. In this algorithm, by integrating the idea of cellular automaton, a novel topology is designed first, which is used to update the velocity of each bat. This topology can help bats to use the information provided by some good neighbors, which means it can help BA to reduce the risk of falling into local optimum. Second, in order to alleviate the difficulty of parameter adjustment in local search equations, a new local search equation is presented based on the number of iterations rather than loudness. Third, the convergence analysis is given based on martingale theory. The computational complex of the proposed algorithm is also discussed. Final, three comparison experiments and two practical optimization problems are conducted to test the performance of the proposed algorithm.

关键词： bat algorithm Cellular automaton Convergence analysis Optimization

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A hybrid grasshopper optimization algorithm with bat algorithm for global optimization

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第3期80卷 3863-3884页

作者： Yue, Shenghan Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation. Additionally, the random strategy is introduced and applied to high quality population to improve the exploitation capability in the searching process. The performance of BGOA is evaluated on 23 benchmark test functions, and compares with genetic algorithm (GA), bat algorithm (BA), moth-flame optimization algorithm (MFO), dragonfly algorithm (DA) and basic GOA. The results establish that the BGOA is able to provide better outcomes than the other algorithms.

关键词： Grasshopper optimization algorithm bat algorithm Levy flight Random strategy Global optimization

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