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Data-driven Koopman fractional order PID control of a MEMS gyroscope using bat algorithm

NEURAL COMPUTING & APPLICATIONS 2023年第13期35卷 9831-9840页

作者： Rahmani, Mehran Redkar, Sangram Arizona State Univ Polytech Sch Ira Fulton Sch Engn Mesa AZ 85212 USA

Data-driven control methods are strong tools due to their predictions for controlling the systems with a nonlinear dynamic model. In this paper, the Koopman operator is used to linearize the nonlinear dynamic model. Generating the Koopman operator is the most important part of using the Koopman theory. Dynamic mode decomposition (DMD) is used to obtain eigenfunction for producing the Koopman operator. Then, a fractional order PID (FOPID) controller is applied to control the linearized dynamic model. A swarm intelligence bat optimization algorithm is utilized to tune the FOPID controller's parameters. Simulation results on micro-electromechanical systems (MEMS) gyroscope under conventional PID controller, FOPID, Koopman-based FOPID controller (Koopman-FOPID), and Koopman-FOPID control optimized by bat algorithm (Koopman-BAFOPID) show that the proposed Koopman-BAFOPID controller has better performance in comparison with three other controllers in terms of high tracking performance, low tracking error, and low control efforts.

关键词： bat algorithm Fractional PID control Koopman operator Dynamic mode decomposition MEMS gyroscope Data-driven method

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Evaluation and optimization of hydrometric locations using entropy theory and bat algorithm (case study: Karkheh Basin, Iran)

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APPLIED WATER SCIENCE 2023年第10期13卷 198页

作者： Eslamian, Saeid Fallah, Khashayar Esmaili Sabzevari, Yaser Isfahan Univ Technol Coll Agr Dept Water Sci & Engn Esfahan Iran

Hydrometric stations are important in most countries because of the application and importance of the data obtained from these stations. It is necessary to choose the best place for their establishment according to the cost of constructing hydrometric stations. The aim and innovation of this research are to optimize the location of hydrometric stations using bat's meta-heuristic algorithm and interpolation methods, which information transfer entropy theory and bat's algorithm were used to maximize the average amount of information transfer entropy. For this purpose, the data of 43 hydrometric stations of Karkheh basin in western Iran in period of 1991-2015 were used. In this research, two scenarios were investigated in order to improve the entropy of information transmission between stations. In the first scenario, using the kriging method to prepare the flow distribution map in the region and choosing normal kriging with spherical variogram as the best model to fit the average annual flow data and using the bat algorithm to increase the correlation coefficient between the data and assuming no none of the available stations, 43 points were used to redeploy stations with higher average entropy in the region. The results of this scenario showed the concentration of new stations in the central and eastern areas of the basin. In the second scenario, the amount of entropy of information transfer at the regional level was calculated and 18 potential points were recommended for the establishment of new stations. The obtained variogram for the discharge of the basin showed that the range of influence is low and it is necessary to establish the stations at a close distance.

关键词： Entropy bat algorithm Kriging Hydrometry Variogram Specific discharge

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A perturbation based modified bat algorithm for priority based covering salesman problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 3825-3849页

作者： Khan, Indadul Dutta, Prasanta Maiti, Manas Kumar Basuli, Krishnendu Chandrakon Vidyasagar Mahavidyalaya Dept Comp Sci Paschim Medinipur 721201 W Bengal India Debra Thana SSK MV Dept Comp Sci Paschim Medinipur W Bengal India Mahishadal Raj Coll Dept Math Purba Medinipur W Bengal India West Bengal State Univ Dept Comp Sci Kolkata W Bengal India

In this study, bat algorithm (BA) is modified along with K-opt operation and one newly proposed perturbation approach to solve the well known covering salesman problem (CSP). Here, along with the restriction of the radial distances of the unvisited cities from the visited cities another restriction is imposed where a priority is given to some cities for the inclusion in the tour, i.e., some clusters to be created where the prioritised cities must be the visiting cities and the corresponding CSP is named as Prioritised CSP (PCSP). In the algorithm, 3-opt and 4-opt operations are used for two different purposes. The 4-opt operation is applied for generating an initial solution set of CSP for the BA and the 3-opt operation generates some perturbed solutions of a solution. A new perturbation approach is proposed for generating neighbour solutions of a potential solution where the exchange of some cities in the tour is made and is named as K-bit exchange operation. The proposed solution approach for the CSP and PCSP is named as the modified BA embedded with K-bit exchange and K-opt operation (MBAKEKO). It is a two-stage algorithm where in the first stage of the algorithm the clustering of the cities is done with respect to a fixed visiting city of each cluster in such a manner that the distances of the other cities of the cluster must lie with in the fixed covering distance of the problem and in the second stage the BA is applied to find the minimum cost Hamiltonian circuit by passing through the visiting cities of the clusters. MBAKEKO is tested with a set of benchmark test problems with significantly large sizes from the TSPLIB. To measure the performance of MBAKEKO, its results are compared with the results of different well-known approaches for CSPs available in the literature. It is observed from the comparison studies that MBAKEKO searches the minimum cost tour for any of the considered instances compared to all other well-known algorithms in the literature. It can

关键词： Combinatorial optimization covering salesmen problem priority based covering salesman problem K-bit exchange operation K-opt bat algorithm

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Coordination of Cyclic crossover and bat algorithm for the Travelling Salesman Problems in Different Environments: A Simulation Approach

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第6期31卷 975-1003页

作者： Pal, Sova Dutta, Prasanta Khan, Indadul Pramanik, Prasenjit Maiti, Ajoy Kumar Maiti, Manas Kumar Yogoda Satsanga Palpara Mahavidyalaya Dept Comp Sci Purba Medinipur 721458 West Bengal India Debra Thana SSK MV Dept Comp Sci Paschim Medinipur West Bengal India Chandrakon Vidyasagar Mahavidyalaya Dept Comp Sci Paschim Medinipur 721201 West Bengal India Vidyasagar Univ Dept Appl Mathemat Oceanol & Comp Programming Midnapore 721102 West Bengal India Raja Narendra Lal Khan Womens Coll Dept Math Midnapore 721102 West Bengal India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 West Bengal India

In this study, the features of cyclic crossover process and K-opt are incorporated in the bat algorithm (BA) to solve the Travelling Salesman Problems (TSP) in different environments. Swap operation and swap sequence are applied for the modification of the different operations of the BA to solve the TSPs. The cyclic crossover operation is applied in a regular interval of iterations on the best found solution and each solution of the final population of BA for the enhancement of the exploration as well as exploitation of the search process. K-Opt operation is applied on the population in each iteration of the BA with some probability for the exploitation. The algorithm is tested with a set of benchmark test instances of the TSPLIB. The algorithm produces exact results for a set of significantly large size problems. For the TSPs in fuzzy environment, a fuzzy simulation approach is proposed to deal with the fuzzy data having linear as well as non-linear membership functions. Also, a rough simulation process is proposed to deal with the TSPs in the rough environment where rough estimation can be done following any type of rough measure. The performance of the algorithm is compared with the state-of-the-art algorithms for the TSPs with crisp cost matrices using different statistical tools.

关键词： Traveling salesmen problem bat algorithm cyclic crossover K-opt fuzzy/rough simulation

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An Improved bat algorithm With Time- Varying Wavelet Perturbations for Cloud Computing Resources Scheduling

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2023年第1期17卷 1-16页

作者： Yu, Fahong Chen, Meijia Yu, Bolin Shanwei Polytech Shanwei Peoples R China Shenzhen Inst Informat Technol Shenzhen Peoples R China

Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.

关键词： bat algorithm Cloud Computing Morlet Wavelet Resources Scheduling Time-Varying Wavelet Wavelet

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Hybridizing Artificial Bee Colony with bat algorithm for Web Service Composition

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Computer Systems Science & Engineering 2023年第8期46卷 2429-2445页

作者： Tariq Ahamed Ahanger Fadl Dahan Usman Tariq Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Management Information Systems College of Business Administration-Hawtat Bani TamimPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Al-TurbahTaiz UniversityTaiz9674Yemen

In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these *** WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)*** challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS *** this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and bat algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global *** bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence *** performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different *** datasets are created from real-world datasets and artificially to form different scale sizes of WSC *** results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to *** signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.

关键词： Internet of things artificial bee colony bat algorithm elitist strategy web service composition

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Optimization of Cogging Torque Based on the Improved bat algorithm

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023年第2期16卷 1-19页

作者： Bai, Wenbo Ran, Huajun China Three Gorges Univ Coll Elect & New Energy Yichang Peoples R China

Permanent magnet motors have the advantages of high output torque, high efficiency, and low noise, but the cogging effect is obvious. The 24-slot 4-pole surface-mounted permanent magnet synchronous motor is taken as an example to reduce the cogging torque of permanent magnet synchronous motors. Firstly, the generation mechanism of cogging torque is analysed based on the energy method, and the pole arc coefficient, air gap length, magnetic pole eccentricity, permanent magnet thickness, and slot opening width are determined as optimisation parameters. Then, a cogging torque optimisation method is further proposed based on the Taguchi method and the response surface method, and the bat algorithm with the Levy flight feature is applied to obtain the optimal solution for the response surface model. Finally, finite element software is used to simulate the optimal motor model. The experimental results show that the efficiency of the motor solved by optimal parameters is increased by 1.6%, the cogging torque is reduced by 82.16%, and the torque ripple is reduced by 8.2%. The optimisation of cogging torque in this paper avoids fluctuations in torque, reduces motor vibration and noise, and improves the control characteristics of the permanent magnet motors drive system, operational reliability, and low-speed performance in the motor speed control system and high accuracy positioning in the position control system.

关键词： Cogging Torque Taguchi Method Response Surface bat algorithm Levy Flight

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Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第1期42卷 411-423页

作者： Bezdan, Timea Zivkovic, Miodrag Bacanin, Nebojsa Strumberger, Ivana Tuba, Eva Tuba, Milan Singidunum Univ Danijelova 32 Belgrade 11000 Serbia

Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain.

关键词： Cloud computing task scheduling multi-objective optimization bat algorithm hybridization

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Global chaotic bat algorithm for feature selection

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JOURNAL OF SUPERCOMPUTING 2022年第17期78卷 18754-18776页

作者： Li, Ying Cui, Xueting Fan, Jiahao Wang, Tan Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China Jilin Univ Northeast Asian Res Ctr Changchun Peoples R China

The wrapper algorithm adopts the performance of the learning algorithm as the evaluation criteria to obtain excellent classification performance. However, the wrapper algorithm is prone to converge prematurely. A global chaotic bat algorithm (GCBA) is put up forward to improve this shortage. First, GCBA applies chaotic map to population initialization to cover the entire solution space. In addition, adaptive learning factors are presented to balance exploration and exploration. The learning factor of local optimal position gradually decreases in the early stage while the learning factor of global optimal position gradually increases in the later stage. Finally, to improve the exploitation, an improved transfer function is proposed, which transfers the continuous space to discrete binary space. GCBA is tested on 14 UCI data sets and 5 gene expression data sets compared with other 6 comparison algorithms. Compared with other algorithms, the results show that GCBA is able to achieve better classification performance.

关键词： Feature selection Wrapper feature selection algorithm bat algorithm Classification Chaotic map

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bat algorithm based on kinetic adaptation and elite communication for engineering problems

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2024年第0期

作者： Yuan, Chong Zhao, Dong Heidari, Ali Asghar Liu, Lei Wang, Shuihua Chen, Huiling Zhang, Yudong Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Univ Leicester Sch Comp & Math Sci Leicester England Xian Jiaotong Liverpool Univ Dept Biol Sci Suzhou Jiangsu Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Zhejiang Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

The bat algorithm, a metaheuristic optimization technique inspired by the foraging behaviour of bats, has been employed to tackle optimization problems. Known for its ease of implementation, parameter tunability, and strong global search capabilities, this algorithm finds application across diverse optimization problem domains. However, in the face of increasingly complex optimization challenges, the bat algorithm encounters certain limitations, such as slow convergence and sensitivity to initial solutions. In order to tackle these challenges, the present study incorporates a range of optimization components into the bat algorithm, thereby proposing a variant called PKEBA. A projection screening strategy is implemented to mitigate its sensitivity to initial solutions, thereby enhancing the quality of the initial solution set. A kinetic adaptation strategy reforms exploration patterns, while an elite communication strategy enhances group interaction, to avoid algorithm from local optima. Subsequently, the effectiveness of the proposed PKEBA is rigorously evaluated. Testing encompasses 30 benchmark functions from IEEE CEC2014, featuring ablation experiments and comparative assessments against classical algorithms and their variants. Moreover, real-world engineering problems are employed as further validation. The results conclusively demonstrate that PKEBA exhibits superior convergence and precision compared to existing algorithms.

关键词： bat algorithm engineering problems global optimization machine learning

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