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An efficient self-adaptive artificial bee colony algorithm for the distributed resource-constrained hybrid flowshop problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 169卷

作者： Tao, Xin-Rui Pan, Quan-Ke Gao, Liang Shanghai Univ Sch Mech & Elect Engn & Automation Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China 99 Shangda Rd Shanghai Peoples R China

Distributed factory processing has attracted the attention and application of many companies because of the low cost and high flexibility. In the present study, the self-adaptive artificial bee colony algorithm (SABC) is presented to solve the distributed resource-constrained hybrid flowshop scheduling (DRCHFS) problems aiming to minimize the makespan. In the proposed algorithm, the two-dimensional vector solution representation is employed. Then, resource constraint in the decoding process is considered. In addition, a self-adaptive perturbation structure and local search strategy based on the critical factory are investigated to enhance searching abilities. The proposed algorithm is tested based on a randomly generated set of the real shop scheduling system, and then numerically analyze and compare the proposed algorithm with the existing heuristic algorithms to verify its effectiveness.

关键词： Hybrid flowshop artificial bee colony algorithm Resource-constrained Distributed Self-adaptive

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Hyperparameter optimization of deep CNN classifier for plant species identification using artificial bee colony algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8827-8838页

作者： Erkan, Ugur Toktas, Abdurrahim Ustun, Deniz Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn TR-70200 Karaman Turkey Tarsus Univ Engn Fac Dept Comp Engn TR-33400 Tarsus Mersin Turkey

Tailoring a deep convolutional neural network (CNN) for an implementation is a tedious and time-consuming task especially in image identification. In this study, an optimization scheme based on artificial bee colony (ABC) algorithm so-called optimal deep CNN (ODC) classifier for hyperparameter optimization of deep CNN is proposed for plant species identification. It is implemented to a ready-made leaf dataset namely Folio containing #637 images with 32 different plant species. The images are undergone various image preprocessing such as scaling, segmentation and augmentation so as to improve the efficacy of the ODC classifier. Therefore, the dataset is augmented from #637 to #15,288 leaf images whose #12,103 images is allocated for training phase and the remainder for testing the ODC. Moreover, a validation process on 20% of the training dataset is performed along with the training phase in both optimization and classification stages. The accuracy and loss performance of the ODC are examined over the training and validation results. The achieved ODC is verified through the test phase as well as by a comparison with the results in the literature in terms of performance evaluation metrics such as accuracy, sensitivity, specificity and F1-score. In order to further corroborate the proposed scheme, it is even subjected to a benchmark with optimization-based studies such as genetic, particle swarm and firefly algorithms through MNIST digit-image dataset. The ODC identifies the leaf images and digit-images with the best accuracy of 98.99% and 99.21% surpassing the state of the arts. Therefore, the proposed ODC is effective and useful in achieving an optimal CNN thanks to ABC algorithm.

关键词： Image processing Image identification Deep convolution neural network Hyperparameter optimization artificial bee colony algorithm

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A hybrid integer programming and artificial bee colony algorithm for staff scheduling in call centers

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 171卷

作者： Xu, Yue Wang, Xiuli Nanjing Univ Sci & Technol Dept Management Sci & Engn 200 Xiaolingwei Nanjing 210094 Jiangsu Peoples R China

The staff scheduling problem (SSP) in call centers is defined as determining working/non-working days for a specific number of employees and their shift types in working days, considering service level requirements, some labor regulations and preferences. The SSP is often difficult to solve to optimality in practice for its complex structure. In this paper, a two-stage approach is proposed to tackle the problem, where the former generates preliminary solutions satisfying all the hard constraints, and the latter refines the solutions by fulfilling soft constraints as much as possible. The proposed approach employs artificial bee colony (ABC) algorithm as the main search framework in both stages, and appropriate neighborhood structures are designed respectively. Specifically, in the latter stage, integer programming (IP) employed under several ruin-and-recreate principles is embedded in the ABC algorithm, which helps intensify and diversify solutions. The experimental results show that the proposed approach is effective and efficient in achieving good solutions for large-scale problem in-stances. In addition, we give some guidance on how to weigh various employees' working preferences and how to balance labor costs and staff satisfaction. Finally, a case study is given to explain how the proposed model and algorithm work for a call center. This study will help call centers make appropriate decisions when planning their employees.

关键词： Call centers Staff scheduling Hybrid algorithm Integer programming artificial bee colony algorithm

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Improved Optimal Control of Transient Power Sharing in Microgrid Using H-Infinity Controller with artificial bee colony algorithm

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ENERGIES 2022年第3期15卷 1043页

作者： Jouda, Mohammed Said Kahraman, Nihan Yildiz Tech Univ Dept Elect & Commun Engn TR-34220 Istanbul Turkey

The microgrid has two main steady-state modes: grid-connected mode and islanded mode. The microgrid needs a high-performance controller to reduce the overshoot value that affects the efficiency of the network. However, the high voltage value causes the inverter to stop. Thus, an improved power-sharing response to the transfer between these two modes must be insured. More important points to study in a microgrid are the current sharing and power (active or reactive) sharing, besides the match percentage of power sharing among parallel inverters and the overshoot of both active and reactive power. This article aims to optimize the power response in addition to voltage and frequency stability, in order to make this network's performance more robust against external disturbance. This can be achieved through a self-tuning control method using an optimization algorithm. Here, the optimized droop control is provided by the H-infinity (H & INFIN;) method improved with the artificial bee colony algorithm. To verify the results, it was compared with different algorithms such as conventional droop control, conventional particle swarm optimization, and artificial bee colony algorithms. The implementation of the optimization algorithm is explained using the time domain MATLAB/SIMULINK simulation model.

关键词： microgrid optimization power sharing droop control artificial bee colony algorithm particle swarm optimization H infinity optimal controller ABC

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Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1108页

作者： Pang, Xinfu Sun, Wei Li, Haibo Wang, Yibao Luan, Changfeng Shenyang Inst Engn Key Lab Energy Saving & Controlling Power Syst Lia Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Co Ltd Yingkou Power Supply Co Yingkou Liaoning Peoples R China

Short-term power load forecasting is essential in ensuring the safe operation of power systems and a prerequisite in building automated power systems. Short-term power load demonstrates substantial volatility because of the effect of various factors, such as temperature and weather conditions. However, the traditional short-term power load forecasting method ignores the influence of various factors on the load and presents problems of limited nonlinear mapping ability and weak generalization ability to unknown data. Therefore, a short-term power load forecasting method based on GRA and ABC-SVM is proposed in this study. First, the Pearson correlation coefficient method is used to select critical influencing factors. Second, the gray relational analysis (GRA) method is utilized to screen similar days in the history, construct a rough set of similar days, perform K-means clustering on the rough sets of similar days, and further construct the set of similar days. The artificial bee colony (ABC) algorithm is then utilized to optimize penalty coefficient and kernel function parameters of the support vector machine (SVM). Finally, the above method is applied on the basis of actual load data in Nanjing for simulation verification, and the results show the effectiveness of the proposed method.

关键词： artificial bee colony algorithm Gray relational analysis Short-term power load forecasting Similar days Support vector machine

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An Improved artificial bee colony algorithm for Model-Free Active Noise Control: algorithm and Implementation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Ren, Xing Zhang, Hongwei Southwest Jiaotong Univ Sch Elect Engn Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 611756 Sichuan Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Guangdong Peoples R China

Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this article, the artificial bee colony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, the FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate, and improve the noise reduction performance. Compared with some closely related population-based model-free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.

关键词： artificial bee colony algorithm Microphones Optimization Convergence Loudspeakers Transfer functions Noise reduction Active noise control (ANC) artificial bee colony (ABC) least mean square (LMS)

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An Efficient artificial bee colony algorithm With an Improved Linkage Identification Method

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4400-4414页

作者： Gao, Hao Fu, Zheng Pun, Chi-Man Zhang, Jun Kwong, Sam Nanjing Univ Posts & Telecommun Coll Automat Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Coll Engn & Sci Melbourne Vic 8001 Australia City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China

The artificial colony (ABC) algorithm shows a relatively powerful exploration search capability but is constrained by the curse of dimensionality, especially on nonseparable functions, where its convergence speed slows dramatically. In this article, based on an analysis of the difference between updating mechanisms that include both all-variable and one-variable updating mechanisms, we find that when equipped with the former strategy, the algorithm rapidly converges to an optimal region, while with the latter strategy, it searches the solution space thoroughly. To utilize multivariable and one-variable updating mechanisms on nonseparable and separable functions, respectively, we embed an improved linkage identification strategy into the ABC by detecting the linkage between variables more effectively. Then, we propose three common strategies for ABC to improve its performance. First, a new approach that considers the historic experiences of the population is proposed to balance exploration and exploitation. Second, a new strategy for initializing scout bees is used to reduce the number of function evaluations. Finally, the individual with the worst performance is updated with a defined probability on multiple dimensions instead of one dimension, causing it to follow the population steps on nonseparable functions. This article is the first to propose all these concepts, which could be adopted for other ABC variants. The effectiveness of our algorithm is validated through basic, CEC2010, CEC2013, and CEC2014 functions and real-world problems.

关键词： Couplings Sociology Statistics artificial bee colony algorithm Iron Convergence Mathematical model artificial bee colony (ABC) economic dispatch problem historic experiences linkage identification strategy (LIS) nonseparable functions scout bees truss structure problem

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A Source-code Aware Method for Software Mutation Testing Using artificial bee colony algorithm

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2022年第3期38卷 289-302页

作者： Arasteh, Bahman Imanzadeh, Parisa Arasteh, Keyvan Gharehchopogh, Farhad Soleimanian Zarei, Bagher Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran Islamic Azad Univ Shabestar Branch Dept Comp Engn Shabestar Iran

The effectiveness of software test data relates to the number of found faults by the test data. Software mutation test is used to evaluate the effectiveness of the software test methods and is one of the challenging fields of software engineering. In order to evaluate the capability of test data in finding the program faults, some syntactical changes are made in the program source code to cause faulty program;then, the generated mutants (faulty programs) and original program are executing with the corresponding test data. One of the main drawbacks of mutation testing is its computational cost. Indeed, high execution time of mutation testing is a challenging research problem. Reducing the time and cost of mutation test is the main objective of this paper. In the traditional mutation methods and tools the mutants are injected randomly in each instructions of a program. Meanwhile, in the real-world program, the probability of fault occurrences in the simple locations (instructions and data) of a program is negligible. With respect to the 80-20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. In the first stage of the proposed method, artificial bee colony optimization algorithm is used to identifying the most fault prone paths of a program;in the next stage, the mutation operators (faults) are injected only on the identified fault-prone instructions and data. Regarding the results of conducted experiments on the standard benchmark programs, Compared to existing methods, the proposed method reduces 28.10% of the generated mutants. Reducing the number of generated mutants will reduce the cost of mutation testing. The traditional mutation testing tools (Mujava, Muclipse, Jester, Jumble) can perform the mutation testing with a lower cost using the method presented in this study.

关键词： Software mutation testing Mutation reduction Fault-prone test paths artificial bee colony algorithm Mutation score

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Silicon carbide photonic crystal optimized by artificial bee colony algorithm 27

Silicon carbide photonic crystal optimized by artificial bee...

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Conference on Integrated Optics - Devices, Materials, and Technologies XXVII

作者： Malheiros-Silveira, Gilliard N. Univ Campinas UNICAMP Dept Commun Sch Elect & Comp Engn FEEC BR-13083852 Campinas Brazil

ISBN: (纸本)9781510659537;9781510659544

The artificial bee colony (ABC) algorithm was applied to solve an inverse design problem of integrated photonics. The ABC algorithm was applied to optimize large photonic bandgaps (PBGs) in 2D-dimensional PhCs composed of silicon carbide (SiC) and air, in a triangular lattice, for transverse magnetic polarization. The band diagram was calculated using the finite element method for the frequency domain. The ABC algorithm reached PhC structures with PBGs as large as 33.94%.

关键词： inverse design artificial bee colony algorithm photonic crystal

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A Two-Stage Genetic artificial bee colony algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards

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IEEE ACCESS 2022年 10卷 131109-131127页

作者： Tayyab, Aisha Saif, Ullah Univ Engn & Technol Dept Ind Engn Taxila 47080 Pakistan

Operating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to cancellations. Thus, this study considers the operating room's capacity and downstream units while making surgery-related decisions. A mixed integer linear programming model for integrated planning consisting of two stages is proposed. The first stage model maximizes the scheduled surgical time of all operating rooms. In contrast, the second stage model aims to minimize the makespan of patients in operating rooms by incorporating sequence-dependent setup time and the capacity constraints of all resources under consideration at both stages. A two-stage genetic artificial bee colony algorithm (TGABC) hybrid of genetic algorithm and artificial bee colony algorithm is proposed to solve the model. Taguchi design of experiments is employed to fine-tune the parameters of the proposed TGABC algorithm. Experiments are designed to evaluate the performance of the proposed TGABC algorithm with generated instances mimicking real data for different-sized problems, and results are presented. The proposed method is compared with the exact method and three standard metaheuristics. It provides near-optimal results in comparatively shorter CPU time. Moreover, it outperforms the genetic algorithm, artificial bee colony algorithm, and simulated annealing in terms of solution quality compared on the considered test instances.

关键词： artificial bee colony algorithm genetic algorithm metaheuristic operating room in health services resource allocation

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