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Neighborhood Search Based Improved bat algorithm for Web Service Composition

Computer Systems Science & Engineering 2023年第5期45卷 1343-1356页

作者： Fadl Dahan Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen

Web services are provided as reusable software components in the services-oriented *** complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service(QoS)*** workflow consists of tasks where many services can be considered for each *** for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial(NP)-hard *** work focuses on the Web Service Composition(WSC)problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the *** proposed algorithm determines the optimal combination of the web services to satisfy the complex user *** also addresses the bat algorithm(BA)shortcomings,such as the tradeoff among exploration and exploitation searching mechanisms,local optima,and convergence *** proposed enhancement includes a developed cooperative and adaptive population initialization *** elitist mechanism is utilized to address the BA convergence *** tradeoff between exploration and exploitation is handled through a neighborhood search *** benchmark datasets are selected to evaluate the proposed bat algorithm’s *** simulation results are estimated using the average fitness value,the standard deviation of the fitness value,and an average of the execution time and compared with four bat-inspired *** is observed from the simulation results that introduced enhancement obtains significant results.

关键词： Cloud computing web service composition bat algorithm serviceoriented architecture

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Solving patient referral problems by using bat algorithm

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TECHNOLOGY AND HEALTH CARE 2020年第S1期28卷 S433-S442页

作者： Yao, Huan-Chung Chen, Pei-Jarn Kuo, Yu-Ting Shih, Chun-Chin Wang, Xuan-Yin Chen, Ping-Shun Chi Mei Med Ctr Dept Med Imaging Tainan Taiwan Southern Taiwan Univ Sci & Technol Dept Elect Engn Tainan Taiwan Chung Yuan Christian Univ Dept Ind & Syst Engn Taoyuan 32023 Taiwan

BACKGROUND: A two-hospital patient referral problem intends to calculate an optimal value of referral patients between two hospitals and to evaluate whether or not the current number of referral patients is too low. OBJECTIVE: The goal of this study is to develop a simulation-based optimization algorithm to find the optimal referral between two hospitals with the unfixed daily patient referral policy. METHODS: This study applied system simulation and a bat algorithm (BA) to build a simulation model in accordance with the status of the two hospitals case and to calculate an optimal value of daily referral patients. RESULTS: Based on the 20 test instances, we verified the stability of this algorithm. The results show that the average magnetic resonance imaging (MRI) patient wait time reduced from 16 days to eight days. The hospital should increase the average total monthly MRI referral patients to 370 under the limitation of the daily referral patients to 25. CONCLUSIONS: This research investigated the two-hospital patient referral problems. We conducted and analyzed a simulation model and improved the case hospital's conditions, enhancing the quality of its medical care. The findings of this study can extend to other departments or hospitals.

关键词： Patient referral problem simulation optimization bat algorithm

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An Improved bat algorithm for More Efficient and Faster Maximum Power Point Tracking for a Photovoltaic System Under Partial Shading Conditions

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IEEE ACCESS 2020年 8卷 96378-96390页

作者： Liao, Chih Yu Subroto, Ramadhani Kurniawan Millah, Ibrahim Saiful Lian, Kuo Lung Huang, Wei-Tzer Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan Natl Changhua Univ Educ Dept Ind Educ & Technol Changhua 500 Taiwan

Solar modules under partial shading (PS) conditions will result in power and voltage characteristic curves (P-VCC) having multiple peaks. If the maximum power point cannot be obtained, the output power of the solar modules will be greatly reduced. Hence, there have been various maximum power point tracking (MPPT) control methods developed to address this problem. One alternative is to employ the meta-heuristic approach (MHA) to track the global maximum power point (GMPP). Recently, a new MHA called bat algorithm (BA) has performed well in the MPPT. Nevertheless, BA may fail to track the GMPP when there are some local maximum power points (LMPPs) close to the GMPP. Also, the tracking time needs to be further reduced to accommodate rapidly changing irradiance. Therefore, a combination of BA with the abandonment mechanism of Cuckoo Search (CS) is proposed to improve the tracking performance of the BA. Both simulation and experimental results show that the proposed method, as compared to BA, yields better accuracy and an improvement of convergence speed of about 35% for various P-VCCs can be achieved. Moreover, the MBA has also been tested against some of the state-of-the-art MPPT algorithms such as Particle Swarm Optimization and Grey Wolf Optimization (GWO), and the results showed the superiority of the proposed method.

关键词： bat algorithm cuckoo search maximum power point tracking partial shading

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Data Clustering Method Based on bat algorithm and Parameters Optimization

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ENGINEERING LETTERS 2019年第1期27卷 241-250页

作者： Zhu, Ling-Feng Wang, Jie-Sheng Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan Peoples R China

The process of dividing a collection of physical or abstract objects into multiple classes of similar objects is called clustering. bat algorithm is a new swarm intelligent optimization algorithm, which is proposed to simulate the foraging behavior of bats and their echolocation ability. The bat algorithm is adopted to solve the data clustering problem and the efficient parameters setting on the bat algorithm is carried out the related research on the pulse rate and pulse loudness. The simulation experiment results show that the reasonable setting parameters can improve the performance of the bat algorithm on the clustering problem.

关键词： clustering bat algorithm parameter optimization

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A Hybrid bat algorithm

ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW

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ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW 2013年第1-2期80卷 1-7页

作者： Fister, Iztok, Jr. Fister, Dusan Yang, Xin-She Univ Maribor Fac Elect Engn & Comp Sci Smetanova 17 SLO-2000 Maribor Slovenia Univ Middlesex Middlesex Univ Sch Sci & Technol London NW4 4BT England

Swarm intelligence is a very powerful technique appropriate to optimization. In this paper, we present a new swarm intelligence algorithm, which is based on the bat algorithm. bat algorithm has been hybridized with differential evolution strategies. This hybridization showed very promising results on standard benchmark functions and also significantly improved the original bat algorithm.

关键词： swarm intelligence bat algorithm differential evolution optimization

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bat algorithm Based Low Power Mapping Methods for 3D Network-on-Chips 35th

Bat Algorithm Based Low Power Mapping Methods for 3D Network...

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35th National Conference of Theoretical Computer Science (NCTCS)

作者： Li, Jiazheng Song, Guozhi Ma, Yue Wang, Cheng Zhu, Baohui Chai, Yan Rong, Jieqi Tianjin Polytech Univ Sch Comp Sci & Software Engn Tianjin 300387 Peoples R China Univ Illinois Sch Informat Sci Champaign IL 61820 USA Yunnan Univ Sch Informat Sci Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789811068935;9789811068928

Mapping a task graph as a distribution of Intellectual Property (IP) cores onto a Network-on-Chip (NoC) is a NP-hard problem that significantly affects the performance metrics of the whole system including power, delay, load balance and heat. Intelligence optimization algorithms are widely used to solve mapping problems. bat algorithm (BA), a novel metaheuristic algorithm mimicking hunting behaviors of bats, which has never been applied in NoCs, is used in low power mapping methods for 3D NoCs in this paper for the first time. The BA based mapping algorithm shows better performance than other mainstream mapping algorithms in terms of the optimization efficiency and power consumption. However, the concept of the basic BA has obvious disadvantages. To improve the basic BA, we propose a Group-Searching bat algorithm (GSBA) that can better utilize individual bats. This improved mapping algorithm performs much better than the traditional BA, especially when the scale of the application graph is large.

关键词： Network-on-Chip Mapping algorithm Low power bat algorithm Parallel computing

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A wrapper approach for feature selection and Optimum-Path Forest based on bat algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第5期41卷 2250-2258页

作者： Rodrigues, Douglas Pereira, Luis A. M. Nakamura, Rodrigo Y. M. Costa, Kelton A. P. Yang, Xin-She Souza, Andre N. Papa, Joao Paulo Univ Estadual Paulista Dept Comp Bauru Brazil Middlesex Univ Sch Sci & Technol London N17 8HR England Univ Estadual Paulista Dept Elect Engn Bauru Brazil

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on bat algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Dimensionality reduction Swarm intelligence bat algorithm Optimum-Path Forest

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A Discrete and Improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 273-286页

作者： Osaba, Eneko Yang, Xin-She Fister, Iztok, Jr. Del Ser, Javier Lopez-Garcia, Pedro Vazquez-Pardavila, Alejo J. TECNALIA Res & Innovat Derio 48160 Spain Middlesex Univ Sch Sci & Technol Hendon Campus London NW4 4BT England Univ Maribor Fac Elect Engn & Comp Sci Smetanova 17 SLO-2000 Maribor Slovenia Univ Deusto Deusto Inst Technol DeustoTech Ave Univ 24 Bilbao 48007 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain BCAM Bilbao 48009 Spain

The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved bat algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

关键词： bat algorithm Medical distribution Rich vehicle routing problem Combinatorial optimization Traveling Salesman Problem

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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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Data-driven reliable prediction of production indicators in the blast furnace using TS fuzzy neural network based on bat algorithm

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年第2期36卷 213-234页

作者： Zhao, Qiang Huo, Yaxin Li, Meng Han, Yinghua Northeastern Univ Sch Control Engn Qinhuangdao Hebei Peoples R China Northeastern Univ Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China

Operational optimisation of the blast furnace (BF) is significant in facilitating smooth operation and reducing the production cost. Operation indicators are pivotal parameters used to measure operating status and reflect the quality of molten iron of the BF. In this study, a data-driven method for the prediction of production indicators is proposed by a novel fuzzy neural network of Takagi-Sugeno with bat algorithm (BA-TS-FNN). In practice, a BF usually works under varying operating conditions due to internal or external factors, redundant features with high dimensionally lead to poor computation time and prediction performance. To solve this problem, mutual information is applied to enhance understanding of the data. Considering that the standard TS-FNN cannot commendably cope with rapid convergence, local optimal, and sensitivity to initial weight problems during learning the prediction model, a bat optimisation algorithm is proposed to search global optimal parameters and improve the convergence rate. Finally, the probability density function (PDF) of modelling error is estimated by the kernel density estimation (KDE) as a criterion to measure the performance of the method. Experiments with industrial data from BF have demonstrated that the proposed method produces higher estimating accuracy than other modelling methods.

关键词： Data-driven model production indicators TS fuzzy neural network (TS-FNN) bat algorithm blast furnace (BF)

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