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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhao, Ming Wang, Meiyu School of Computer Science and Engineering Shenyang Jianzhu University Liaoning China

ISBN: (纸本)9798331506582

Aiming at the shortcomings of the standard artificial bee colony (ABC) algorithm due to weak exploitation capability and slow convergence speed caused by one-dimension search, an ABC algorithm with dynamic dimension search (DDSABC) is proposed. Firstly, a dynamic selection mechanism for dimension search strategy is designed, where one-dimension search is used in the early stage of search to exploit globally diverse individuals, and multi-dimension search is used in the late stage to explore convergent individuals;an elite moderately guided dimension selection mechanism is designed for multi-dimension search, where half of the dimensions are selected under the guidance of the elite mean to accelerate the evolution of potential dimensions, and the other half are selected by a randomized way to maintain a moderate randomness;in addition, suitable search equations are selected for the different characteristics of the two dimension search strategies to ensure the balance between exploration and exploitation capabilities. Finally, on 30 complex problems from the CEC2014 test set, ablation experiments and comparative experiments with the standard ABC and its 5 variants are analyzed, and the results show that the proposed algorithm in this paper performs better in terms of solution accuracy, stability and convergence speed. © 2024 IEEE.

关键词： artificial bee colony algorithm dimension selection dynamic dimension search strategy elite guidance multi-dimension search

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A Discrete Multi‑Objective artificial bee colony algorithm for a Real‑World Electronic Device Testing Machine Allocation Problem

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Chinese Journal of Mechanical Engineering 2022年第6期35卷 136-150页

作者： Jin Xie Xinyu Li Liang Gao State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China

With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human *** have higher quality requirements for electronic *** device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the *** the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be *** study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine ***,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing ***,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,*** experiments were conducted to evaluate the performance of the proposed *** experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency *** results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.

关键词： Electronic device Machine allocation Multi-objective optimization artificial bee colony algorithm

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Discrete artificial bee colony algorithm for Lot-streaming Flowshop with Total Flowtime Minimization

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Chinese Journal of Mechanical Engineering 2012年第5期25卷 990-1000页

作者： SANG Hongyan GAO Liang PAN Quanke State Key Lab. of Digital Manufacturing Equipment & Technology Huazhong University of Science and Technology Wuhan 430074 China School of Mathematics Science Liaocheng University Liaocheng 252059 China School of Computer Science Liaocheng University Liaocheng 252059 China

Unlike a traditional flowshop problem where a job is assumed to be indivisible, in the lot-streaming flowshop problem, a job is allowed to overlap its operations between successive machines by splitting it into a number of smaller sub-lots and moving the completed portion of the sub-lots to downstream machine. In this way, the production is accelerated. This paper presents a discrete artificial bee colony （DABC） algorithm for a lot-streaming flowshop scheduling problem with total flowtime criterion. Unlike the basic ABC algorithm, the proposed DABC algorithm represents a solution as a discrete job permutation. An efficient initialization scheme based on the extended Nawaz-Enscore-Ham heuristic is utilized to produce an initial population with a certain level of quality and diversity. Employed and onlooker bees generate new solutions in their neighborhood, whereas scout bees generate new solutions by performing insert operator and swap operator to the best solution found so far. Moreover, a simple but effective local search is embedded in the algorithm to enhance local exploitation capability. A comparative experiment is carried out with the existing discrete particle swarm optimization, hybrid genetic algorithm, threshold accepting, simulated annealing and ant colony optimization algorithms based on a total of 160 randomly generated instances. The experimental results show that the proposed DABC algorithm is quite effective for the lot-streaming flowshop with total flowtime criterion in terms of searching quality, robustness and effectiveness. This research provides the references to the optimization research on lot-streaming flowshop.

关键词： lot-streaming flowshop scheduling artificial bee colony algorithm total flowtime

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Application of artificial bee colony algorithm to Maximum Likelihood DOA Estimation

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Journal of Bionic Engineering 2013年第1期10卷 100-109页

作者： Zhicheng Zhang Jun Lin Yaowu Shi College of Communication Engineering Jilin University Changchun 130012 P. R. China College of Instrumentation & Electrical Engineering Jilin University Changchun 130061 P. R. China

Maximum Likelihood （ML） method has an excellent performance for Direction-Of-Arrival （DOA） estimation, but a mul- tidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the artificial bee colony （ABC） algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the be- havior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the con- ventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio （SNR）, and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investi- gated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.

关键词： DOA estimation maximum likelihood artificial bee colony algorithm bio-inspired computing

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Indoor evacuation model based on visual-guidance artificial bee colony algorithm

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Building Simulation 2022年第4期15卷 645-658页

作者： Xinlu Zong Aiping Liu Chunzhi Wang Zhiwei Ye Jiayuan Du College of Computer Science and Technology Hubei University of TechnologyNo.28Nanli RoadHong-shan DistrictWuchangWuhanHubei ProvinceChina

Research on evacuation simulation and modeling is an important and urgent issue for emergency *** paper presents an evacuation model based on cellular automata and social force to simulate the evacuation *** force of target position,repulsive forces of individuals and obstacles,as well as congestion are considered in order to simulate the interaction among evacuees and the changing environment.A visual-guidance-based artificial bee colony algorithm is proposed to optimize the evacuation *** evacuee moves toward exits with the guidance of leading bee in his/her visual *** leading bee is selected according to comprehensive factors including distance from the current individual,the number of obstacles and congestion,which avoids the randomness of roulette mechanism used by basic artificial bee colony *** experimental results indicate that the proposed model and algorithm can achieve effective performances for indoor evacuation problems with a large number of evacuees and obstacles,which accords with the actual evacuation situation.

关键词： evacuation model artificial bee colony algorithm visual guidance cellular automata social force

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Performance analysis and multi-objective optimization of an organic Rankine cycle with binary zeotropic working fluid employing modified artificial bee colony algorithm

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2019年第4期136卷 1645-1665页

作者： Sadeghi, Sadegh Maghsoudi, Peyman Shabani, Bahman Gorgani, Hamid Haghshenas Shabani, Negar Iran Univ Sci & Technol Sch Engn Dept Mech Engn Tehran Iran UT Coll Engn Sch Mech Engn Tehran Iran RMIT Univ Sch Engn Melbourne Vic Australia Sharif Univ Technol Engn Graph Ctr Tehran Iran Persian Gulf Univ Sch Sci Bushehr Iran

From a thermal point of view, zeotropic mixtures are likely to be more efficient than azeotropic fluids in low-temperature power cycles for reduction in exergy destruction occurring during heat absorption/rejection processes due to their suitable boiling characteristics. In this study, comprehensive energetic and exergetic analyses are mathematically performed for an organic Rankine cycle (ORC) system employing a potential binary zeotropic working fluid, namely R717/water. For this purpose, initially mass, energy, and exergy balance equations are derived. With regard to the similarity in molar mass of R717 (17.03 g mol(-1)) and water (18.01 g mol(-1)), there is no need to alter the size of the ORC components such as turbine and pump. In order to achieve the optimal thermal and exergy efficiencies of the ORC system, modified version a powerful and relatively new optimization algorithm called artificial bee colony (ABC) is used taking into account different effective constraints. The main motivation behind using ABC lies on its robustness, reliability, and convergence rate speed in dealing with complicated constrained multi-objective problems. Convergence rates of the algorithm for optimal calculation of the efficiencies are presented. Subsequently, due to the importance of exergy concept in ORC systems, exergy destructions occurring in the components are computed. Finally, the impacts of pressure, temperature, mass fraction, and mass flow rate on the ORC thermal and exergy efficiencies are discussed.

关键词： Binary zeotropic mixture Organic Rankine cycle Thermal efficiency Exergy efficiency Multi-objective optimization artificial bee colony algorithm

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A novel approach to speckle noise filtering based on artificial bee colony algorithm: An ultrasound image application

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013年第3期111卷 561-569页

作者： Latifoglu, Fatma Erciyes Univ Fac Engn Dept Biomed Engn Kayseri Turkey

In this study a novel approach based on 2D FIR filters is presented for denoising digital images. In this approach the filter coefficients of 2D FIR filters were optimized using the artificial bee colony (ABC) algorithm. To obtain the best filter design, the filter coefficients were tested with different numbers (3 x 3, 5 x 5, 7 x 7, 11 x 11) and connection types (cascade and parallel) during optimization. First, the speckle noise with variances of 1, 0.6, 0.8 and 0.2 respectively was added to the synthetic test image. Later, these noisy images were denoised with both the proposed approach and other well-known filter types such as Gaussian, mean and average filters. For image quality determination metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) were used. Even in the case of noise having maximum variance (the most noisy), the proposed approach performed better than other filtering methods did on the noisy test images. In addition to test images, speckle noise with a variance of 1 was added to a fetal ultrasound image, and this noisy image was denoised with very high PSNR and SNR values. The performance of the proposed approach was also tested on several clinical ultrasound images such as those obtained from ovarian, abdomen and liver tissues. The results of this study showed that the 2D FIR filters designed based on ABC optimization can eliminate speckle noise quite well on noise added test images and intrinsically noisy ultrasound images. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： 2D FIR filter artificial bee colony algorithm Mean square error Peak signal-to-noise ratio Speckle noise Ultrasound image denoising

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Optimisation of Flight and Maintenance Planning for Defence Aviation with Modified artificial bee colony algorithm

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DEFENCE SCIENCE JOURNAL 2021年第1期71卷 3-11页

作者： Balakrishnan, N. Shah, Malak Anupama, K. R. Sharma, Nitin BITS Pilani KK Birla Goa Campus Sancoale 403726 Goa India BITS Pilani Dept Elect & Elect Engn KK Birla Goa Campus Sancoale 403726 Goa India

The planning of flight operations and maintenance is a crucial activity for both commercial and military aircraft. Military aircraft have to be always mission-ready. The task of ensuring this can become quite challenging when several operational requirements and maintenance constraints are to be fulfilled simultaneously. This paper, therefore, addresses the optimisation of flight and maintenance planning (FMP) when several diverse factors such as aircraft flying hours (AFH), flight cycles (FC), calendar life, annual flying requirement (AFR), etc. are to be factored in. Such a problem has not been considered previously. Because the problem can become unwieldy to solve by other methods, two schemes, that is, the genetic algorithm (GA) and modified artificial bee colony (ABC) algorithm for constrained optimisation have been utilised. The objective is to maximise the utilisation rate (UR) of aircraft, while also satisfying other operational and maintenance constraints. The algorithm is tested on a fleet of eight aircraft. In addition to a one-year planning period, a planning horizon of ten years has also been simulated. The results show that both the GA and modified ABC algorithm can be effectively used to solve the FMP problem.

关键词： Optimisation Flight Maintenance planning Defence aviation Genetic algorithm artificial bee colony algorithm

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A hybrid discrete artificial bee colony optimization algorithm for the no-wait job shop problem with tardiness criterion

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2025年 19卷

作者： Huang, Xuemei Yin, Jie Deng, Guanlong Yantai Port Wanhua Ind Pk Terminal Co Ltd Yantai Peoples R China Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China

The no-wait job shop scheduling problem (NWJSP) plays a crucial role in industrial production and is an NP-hard problem. We propose a hybrid discrete artificial bee colony (HDABC) algorithm for solving the NWJSP with the total tardiness criterion. In the proposed algorithm, we first design multiple discrete operations which combine with the basic framework of the artificial bee colony algorithm. Furthermore, we propose a new selection method that allows onlooker bee to select better food sources. To determine the start times of the jobs, we introduce and adapt the left timetabling method for the tardiness objective under consideration. Experimental results show that the HDABC algorithm has better search capability than two well-performing ABC algorithms as well as an iterated greedy algorithm in solving the NWJSP with the total tardiness criterion.

关键词： Job shop no-wait artificial bee colony algorithm scheduling total tardiness

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An artificial bee colony algorithm approach for unrelated parallel machine scheduling with processing set restrictions, job sequence-dependent setup times, and due date

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第9-12期77卷 2105-2115页

作者： Caniyilmaz, Erdal Benli, Betul Ilkay, Mehmet S. Erciyes Univ Fac Engn Dept Ind Engn TR-38039 Kayseri Turkey Erciyes Univ Fac Econ & Adm Sci Dept Business TR-38039 Kayseri Turkey

The problem dealt with in this study has job sequence-dependent setup times under suitable machine constraints and due date constraints. The performance criterion for this problem is to minimize the sum of makespan and total tardiness. In the literature, an application of ABC algorithm for the problems which includes all the properties that we have dealt has not been discussed. Because there is no appropriate test data, a real-life data was collected from a factory. In this study, a new approach has been proposed for the solution with meta-heuristics of unrelated parallel machine scheduling problems which is a combinatorial problem. This new neighborhood approach provides different machine assignments for every candidate job sequences. This approach is used by integrating into ABC and GA. To evaluate the performances of the algorithms, the real-life problem was solved by using ABC and GA algorithms under similar conditions. It was found that all jobs can be completed in two shifts without the need for a third shift. Computational results show that ABC algorithm has better performance than GA.

关键词： Scheduling artificial bee colony algorithm Combinatorial artificial bee colony Unrelated parallel machine scheduling Meta-heuristics

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