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Adaptive bat algorithm Based on Historical Population Control

AUTOMATIC CONTROL AND COMPUTER SCIENCES 2022年第5期56卷 438-446页

作者： Geng, Yanxiang Zhang, Liyi Guo, Jianing Jiang, Yin Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China

bat algorithm, as a new type of swarm intelligence algorithm, has excellent optimization ability and wide application space, as well as the convergence speed and accuracy problem exists, and is easy to be trapped in local most defects first. Inspired by backtracking algorithm, aiming at the shortcomings of bat algorithm, this paper proposes the concept of establishing historical population. When updating the algorithm, the new generation bats need to consider the position information of excellent individuals in the historical population to produce the clustering effect. At the same time, the self-learning factor can effectively control the bat algorithm from global search to local search. The improved algorithm increases the interaction of bat information and the communication between individuals, and increases the interference of bat population. The simulation results show that the improved algorithm has good robustness, reliability and stability, accelerates the search ability of the algorithm, and improves the convergence speed and accuracy.

关键词： bat algorithm backtracking strategy historical population cross

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Planted (l, d) motif search using bat algorithm with inertia weight and opposition based learning

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International Journal of Information Technology (Singapore) 2022年第7期14卷 3555-3563页

作者： Theepalakshmi, P. Reddy, U. Srinivasulu Department of Computer Applications National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India

Understanding the molecular mechanism of transcriptional regulation is the fundamental task of identifying and characterizing gene regulatory binding motifs using computational techniques. In computational biology, finding the planted (ℓ, d) motif search is a challenging task, even though it was addressed by various computational intelligence techniques, still it has the limitations of less accuracy and more computational time. bat algorithm (BA) and together with two more techniques, namely inertia weight (IW) and opposition based learning (OBL) have been proposed to search planted (ℓ, d) motif. IW is used to control BA's exploration and exploitation capabilities, and OBL is in charge of reducing execution time by modifying the random directions. Particularly the constant, random inertia weight and quasi OBL improve the accuracy and execution time during the simulation. The proposed model was tested with the simulated and real dataset;it is 25 times faster than the hybrid method and 11 times faster than the DNAMotif_PSO for larger (ℓ, d) values. In the real dataset [Escherichia coli cyclic AMP receptor protein (CRP)], the proposed model gives better accuracy when compared to the state-of-the-art methods and can find the transcription factor binding sites accurately in the simulated and real datasets with minimal time. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： bat algorithm Inertia weight IOBA algorithm Opposition based learning (OBL) Planted (ℓ, d) motif search

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Optimization design of magnetic gear integrated PMSG using improved bat algorithm for wave energy conversion

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2021年第3期66卷 525-542页

作者： Fang, Hong Wei Wang, Yu Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The magnetic gear integrated permanent magnet synchronous generator (MG-PMSG) can reduce the acoustic noise and mechanical loss, which are caused by the mechanical gear box. It also has the merits of increasing efficiency and reducing system volume when it is used for wave energy conversion system. In this paper, an improved bat algorithm (BA) based on velocity weighting factor is proposed. The improved BA is applied for the optimization design of permanent magnet (PM) to reduce the cogging torque of MG-PMSG. The numerical model is constructed by response surface methodology (RSM). The influences of key pole shape parameters on cogging torque were investigated, including the eccentric distance, the pole-arc coefficient and the permanent magnet thickness. A global optimization design is then carried out by using the improved BA, so that the magnet dimensions corresponding to the optimal cogging torque are obtained. Finally, the performances of the MG-PMSG with the optimized permanent magnet are analyzed by finite element method. Results show that cogging torque, steady torque ripple and back electromotive force (EMF) waveform distortion of the optimized MG-PMSG are reduced.

关键词： Magnetic gear wave energy conversion cogging torque bat algorithm response surface methodology

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An efficient battery swapping and charging mechanism for electric vehicles using bat algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Vani, batchu Veena Kishan, Dharavath Ahmad, Md Waseem Reddy, B. Naresh Kumar Natl Inst Technol Karnataka Dept Elect & Elect Engn Surathkal India Natl Inst Technol Tiruchirappalli Dept Elect & Commun Engn Tiruchirappalli Tamil Nadu India

The recent surge in electric vehicle (EV) adoption has presented various challenges, notably in the charging and discharging processes of EV batteries, each characterized by unique traits. While conventional charging stations remain popular, battery swap stations (BSS) offer a compelling alternative, addressing issues like prolonged waiting times and potential battery degradation from fast charging. BSS, with its extensive array of battery systems, ensures efficient services for EVs. However, meticulous planning for the charging and discharging operations is imperative for both BSS and the overall grid to guarantee optimal functionality. This paper proposes an efficient approach to enhance the efficiency of battery swapping and charging mechanisms (BSCM) for electric vehicles, leveraging the bat algorithm. The BSCM is conceived as a system that incorporates both the battery swapping mechanism (BSM) and the battery charging mechanism (BCM). The key contribution lies in designing an effective BSCM where the BSM functions as a manager, handling battery swapping requests from EV users, while the BCM acts as a supporter, interfacing with the grid to regulate battery charging and discharging power. To efficiently address the mixed-integer nonlinear program (MINLP) inherent in this system, a bat algorithm is developed. The results clearly demonstrate the effectiveness of the proposed algorithm in efficiently addressing large-scale problems, producing solutions that closely approach optimality. It promptly achieves a substantial reduction in battery swapping energy by 30% and 24%, respectively, and significantly enhances charging station utilization by 25% and 21% compared to the LSTM-Based Rolling Horizon Approach and Bilevel Optimization Approach. Additionally, the algorithm showcases remarkable improvements in battery swapping performance, boasting a 25% and 19% enhancement, and noteworthy increases in charging station utilization by 20% and 17% compared to the aforement

关键词： Energy optimization bat algorithm Charging mechanism battery swapping Electric Vehicles (EVs)

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A new method for evaluating roundness error based on improved bat algorithm

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MEASUREMENT 2024年 238卷

作者： He, Qingze Zheng, Peng Lv, Xingchen Li, Jicun Li, Yan Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450001 Henan Peoples R China

Roundness error is one of the core indicators for evaluating the geometric accuracy of round parts mechanical products, directly affecting product performance and service life. In the field of metrology, the accurate, rapid and standardized assessment of roundness error has always been a hot topic. This paper proposed a new method for evaluating roundness error based on improved bat algorithm. This method was based on the new geometric product specification for extraction, filtering and fitting. Based on the advantages of solving optimization problems using the bat algorithm, transformed the roundness error evaluation problem using the minimum zone method into a problem of using the bat algorithm to find the center of the minimum zone circle, then further solved for the roundness error value. Also, this algorithm effectively avoided falling into local optimal solutions by introducing chaotic inertia weights during the velocity update phase, it improved the accuracy and speed of the evaluation. Introduced adaptive parameters during the loudness and emission rate update phases to enhance the algorithm's global search capability, it enhanced the stability of the algorithm. The experimental results indicated, the efficiency of roundness error evaluation in the new method was significantly better than that of the genetic algorithm, the simplex algorithm and the emperor penguin algorithm. There was a significant improvement in evaluation accuracy and stability. The feasibility of this method in roundness error evaluation using the minimal zone method was validated.

关键词： Roundness error bat algorithm Chaos inertia weight Adaptive parameters

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Fuzzy bat algorithm for improved Clustering of Convolutional Neural Network Data 37

Fuzzy Bat Algorithm for improved Clustering of Convolutional...

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37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Tiacharoen, Somporn King Mongkuts Univ Technol North Bangkok Elect Engn Technol Coll Ind Technol Bangkok Thailand

ISBN: (数字)9781665485593

ISBN: (纸本)9781665485593

This article is expanded the technique for clustering the images from Convolutional Neural Network (CNN) with bat algorithm (BA). Varian images extract the features from CNN for identifting each image data. BA categorize those image data for improved clustering, assist with fuzzy logic systems. Computational result are comparative experiment results between CNN and Fuzzy BA, mean squared error and root mean squared error respectively.

关键词： Convolutional Neural Network bat algorithm Fuzzy Clustering Machine learning

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Improved Binary bat algorithm for Optimally Placing Multiple DGs in RDN 10

Improved Binary Bat Algorithm for Optimally Placing Multiple...

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10th IEEE Power India International Conference (PIICON)

作者： Saxena, N. Pandit, M. Srivastava, L. Jabalpur Engn Coll Elect Engn Dept Jabalpur India Madhav Inst Sci & Technol Elect Engn Dept Gwalior India

ISBN: (纸本)9781665459303

Identification of optimal location for placing distributed generators for the mitigated power losses and upgraded voltage profile in radial distribution network is presented in this article. Load flow solution is obtained using Backward-Forward Sweep method. Improved Binary bat algorithm, as optimization technique, is used to evaluate the possible best location and magnitude of dispersed generators. Analysis is done by simulating over IEEE-33 and IEEE-69 bus system. Results signifies the loss reduction with voltage profile improvement.

关键词： Distributed Generation Backward Forward Sweep algorithm BIBC-BCBV matrix bat algorithm

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Improved Directional bat algorithm Based Electric Power Dispatch

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2021年第19-20期48卷 2089-2105页

作者： Singh, Diljinder Dhillon, Jaspreet Singh St Longowal Inst Engn & Technol Sangrur Punjab India

No single metaheuristic search algorithm can be adjudged universally best general-purpose optimizer. The performance of search algorithms mainly depends upon the weightage assigned to global and local search strategies. This paper proposed an improved directional bat optimizer to minimize the operating cost of the electric power dispatch (EPD) problem that establishes a balance between global and local search strategies. Improved directional bat algorithm exploits directional echolocation bat behavior, directional exploration, neighborhood search and opposition based learning for generation jumping. The directional bat algorithm acts as a global search tool whereas exploration in each direction and neighborhood search performs local search. Opposition learning improves convergence with diversity. An effect of valve-point loading introduces a discontinuity in cost characteristics. The EPD problem addresses energy balance, generator capacity, ramp-rate limits and prohibited operating zones (POZ) avoidance constraints. An iterative technique handles energy balance constraint. The generation is adjusted to avoid the violation of generation capacity, ramp-rate limit and POZ constraints. The proposed algorithm is verified on various electric power systems. The results verify that the proposed algorithm is a potential algorithm to solve EPD problems as it competes with recent existing algorithms undertaken for comparison

关键词： economic power dispatch directional echolocation bat algorithm exploration opposition-based learning local search

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Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem

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SOFT COMPUTING 2021年第8期25卷 6445-6467页

作者： Jiang, Manrui Liu, Weiyi Xu, Wen Chen, Wei Capital Univ Econ & Business Sch Management Engn Beijing Peoples R China Capital Univ Econ & Business Sch Finance Beijing Peoples R China

This paper deals with a multiperiod multiobjective fuzzy portfolio selectiossn problem based on credibility theory. A credibilistic multiobjective mean-VaR model is formulated for the multiperiod portfolio selection problem, whereby the return is quantified by the credibilistic mean and the risk is measured by the credibilistic VaR. We also consider liquidity, cardinality, and upper and lower bound constraints to obtain a more realistic model. Furthermore, to solve the proposed model efficiently, an improved multiobjective bat algorithm termed IMBA is designed, in which three new strategies, i.e., the global best solution selection strategy, candidate solution generation strategy, and competitive learning strategy, are proposed to increase the convergence speed and improve the solution quality. Finally, comparative experiments are presented to show the applicability and superiority of the proposed approaches from two aspects. First, the designed IMBA is compared with seven typical algorithms, i.e., multiobjective particle swarm optimization, multiobjective artificial bee colony, multiobjective firefly algorithm, multiobjective differential evolution, multiobjective bat, the non-dominated sorting genetic algorithm (NSGA-II) and strength pareto evolutionary algorithm 2 (SPEA2), on a number of benchmark test problems. Second, the applicability of the proposed model to practical applications of portfolio selection is given under different circumstances.

关键词： Multiperiod portfolio selection Credibility measure Multiobjective optimization Value at risk bat algorithm

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Energy efficient fuzzy clustering and routing using bat algorithm

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WIRELESS NETWORKS 2021年第4期27卷 2813-2828页

作者： Lipare, Amruta Edla, Damodar Reddy Dharavath, Ramesh Natl Inst Technol Ponda 403401 Goa India Indian Inst Technol ISM Dhanbad Bihar India

Clustering of sensor nodes is one of the prominent methods applied to Wireless Sensor Networks (WSN). In the cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes are composed of limited battery power. Therefore, energy efficiency in WSN is crucial. A load of sensor node and its distance from base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSN. In this paper, we propose a load-balanced clustering algorithm using Fuzzy C means (FCM) algorithm and an energy-efficient routing approach using bat-algorithm (FC-Rbat). The cluster heads (CHs) are selected according to the score of the sensor node from each cluster. After selection of the CHs, the bat-inspired routing algorithm is applied on the CHs. The best routing path from each CH to the BS is obtained from the proposed approach. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network and the standard deviation of a load of the sensor node. It is observed that FC-Rbat outperforms compared algorithms, namely EAUCF, DUCF and SGA, under the evaluation factors.

关键词： bat algorithm Clustering Energy efficiency Fuzzy system Load balancing Wireless Sensor Networks

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