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Minimizing fuzzy makespan in a distributed assembly flow shop by using an efficient artificial bee colony algorithm

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 7025-7046页

作者： Zuo, Yandi Wang, Pan Fan, Zhun Li, Ming Guo, Xinhua Gao, Shijie Wuhan Univ Technol Sch Automat Wuhan Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou Peoples R China Anhui Polytech Univ Sch Econ & Management Wuhu Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China

Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades;however, the distributed AFSP with DPm -> 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented;a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed;the historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances.

关键词： distributed assembly flow shop scheduling uncertainty artificial bee colony algorithm fuzzy makespan

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A multi-motor speed synchronization control enhanced by artificial bee colony algorithm

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MEASUREMENT & CONTROL 2023年第1-2期56卷 133-146页

作者： Wang, He Du, Hualong Cui, Qiuyu Lui, Pengfei Ma, Xin Univ Sci & Technol Liaoning Sch Mech Engn & Automat 189 Qianshan Middle Rd Anshan 114051 Liaoning Peoples R China

Relative coupling control (RCC) structure is widely used for multi-motor speed synchronization control. To improve the performance of multi-motor synchronous control, artificial bee colony (ABC) algorithm is developed and applied for enhancing RCC structure. The RCC structure optimized by the ABC algorithm is called the RCC-ABC structure. In the RCC-ABC structure, the ABC algorithm optimizes both the fixed compensation factor in the speed compensator and the parameters of the PID controller acting as a tracking controller. Simulation experiments based on a four-motor system are conducted to validate the efficiency of the RCC-ABC structure. Simulation results show that the RCC-ABC structure has better synchronization and tracking performance compared to the RCC structure.

关键词： Multi-motor synchronization control relative coupling control structure parameter optimization artificial bee colony algorithm

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An Enhanced artificial bee colony algorithm for Constraint Optimization

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ENGINEERING LETTERS 2024年第2期32卷 276-283页

作者： Wang, Zhen Kong, Xiangyu Xianyang Normal Univ Inst Math & Stat Xianyang 712000 Shaanxi Peoples R China

the complexity of constraint optimization problem constructed based on the engineering problem, it is usually hard to solve. So as to solve the constrained optimization problem effectively, an enhanced artificial bee colony algorithm is put forward in this article. At the employed bee stage, the proposed algorithm introduces a new search equation and constraint handling strategy by identifying the current state of the population, and guide the population to access the feasible area quickly. Moreover, at the onlooker bee stage, the global optimal solution is used to lead the individuals to search the feasible region deeply, which improves the development capability of algorithm. The experiments are operated on 20 test functions in CEC 2006 and 2 realistic engineering optimization problems. All results confirm that the use of proposed algorithm for solving constraint optimization is valid.

关键词： Index Terms-constraint optimization artificial bee colony algorithm feasibility rules best-guided searching

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An improved many-objective artificial bee colony algorithm for cascade reservoir operation

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NEURAL COMPUTING & APPLICATIONS 2023年第18期35卷 13613-13629页

作者： Wang, Hui Wang, Shuai Wei, Zichen Zeng, Tao Ye, Tingyu Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China

artificial bee colony (ABC) has shown good performance on single-objective and ordinary multi-objective optimization problems. However, ABC faces some difficulties with increasing number of objectives. The selection pressure based on Pareto dominance degrades severely. The original ABC shows weak exploitation ability and slow convergence speed. To help ABC solve many-objective optimization problems (MaOPs), this paper proposes an improved many-objective ABC algorithm based on decomposition and dimension learning (called MaOABC-DDL). Firstly, an MaOP is converted to several sub-problems by the decomposition. The original fitness function is not available because of multiple objective values. Then, a new fitness function is defined based on the ranking of each objective. Solutions with good fitness values are selected to form an elite set. To improve the convergence, an elite set guided search strategy and dimension learning are designed for the employed bee and onlooker bee stages, respectively. Moreover, the scout bee stage is modified to dynamically allocate computing resources. To verify the performance of MaOABC-DDL, the DTLZ and MaF benchmark problems with 3, 5, 8, and 15 objectives are tested. Results show that MaOABC-DDL can obtain better performance when compared with seven other many-objective evolutionary algorithms. Finally, MaOABC-DDL is applied to cascade reservoir operation. Simulation results show that our approach still achieves promising performance.

关键词： artificial bee colony algorithm Swarm intelligence Many-objective optimization Cascade reservoir operation

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artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2021年第6期20卷 2530-2562页

作者： Tefek, Mehmet Fatih Osmaniye Korkut Ata Univ Dept Comp Engn Osmaniye Turkey

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E-04, 9.8257E-04 and 2.4251E-03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it ac

关键词： artificial bee colony algorithm Local search Photovoltaic systems Photovoltaic module parameter estimation

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Improved multi-objective artificial bee colony algorithm for parallel machine lot-streaming scheduling problem with limited and unequal sub-lots

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期183卷

作者： Zhu, Yingying Tang, Qiuhua Zhang, Liping He, Ming Kapenda, Jerry Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Precis Mfg Inst Wuhan 430081 Peoples R China

Job splitting is a full-time effective measure used to improve corporate competitiveness, especially when the lot size of jobs is considerably large. This work investigates a parallel machine lot-streaming scheduling problem, where unequal sub-lot is considered and the number of sub-lots is strictly limited by that of cutters. Focus on this problem, a mixed-integer linear programming model is formulated to minimize makespan and due time deviation simultaneously, to scrutinize the coupling relationship among decision variables, i.e., the number and size of sub-lots, the machine allocation, and the processing order of all sub-lots. Then, an improved multi-objective artificial bee colony algorithm (IMOABC) with two categories of improvements is developed to obtain highquality Pareto front solutions. Specifically, a semi-random initialization is proposed to balance the workload as evenly as possible, which includes a half-normal distribution strategy for determining the number of utilized sub-lots, an equal-probability lot-splitting strategy for determining the size of each sub-lots, and a machine allocation strategy to synchronize the completion time of all machines. Besides, a multipoint preservative crossover is designed to enhance diversity in the employed bee phase, a sub-lot adjustment operator is proposed to implement objective-oriented local search in the onlooker bee phase, and an individual restart mechanism is adopted to avoid being trapped in local optimum in the scout bee phase. Extensive experiment results demonstrate that IMOABC significantly outperforms the other six state-of-the-art algorithms in terms of diversity and convergence.

关键词： Parallel machine lot-streaming scheduling Limited and unequal sub-lots Due time deviation artificial bee colony algorithm Semi-random initialization

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The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

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Geodesy and Geodynamics 2023年第3期14卷 244-253页

作者： Leyang Wang Shuhao Han Faculty of Geomatics East China University of TechnologyNanchang 330013China Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake Ministry of Natural ResourcesNanchang 330013China

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error *** improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine *** experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula *** parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

关键词： Mixed additive and multiplicative random error Parameter estimation Accuracy evaluation artificial bee colony algorithm Bootstrap method

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Fractional-order artificial bee colony algorithm with application in robot path planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期306卷 47-64页

作者： Cui, Yibing Hu, Wei Rahmani, Ahmed Cent Lille CRIStAL UMR CNRS 9189 F-59651 Villeneuve Dascq France Beijing Jiaotong Univ Inst Syst Sci Beijing 100044 Peoples R China

artificial bee colony (ABC) algorithm is a popular meta-heuristic optimization algorithm inspired by the foraging behaviors of honeybees. Although ABC has outstanding exploration ability, it has certain weak-ness like poor exploitation capability and low solution precision. Therefore, to tackle these weakness and improve the performance, an ABC algorithm based on fractional-order calculus (FOABC) is proposed. More specially, the memory properties of fractional-order calculus is incorporated into onlooker bee phase to improve the local-search capability. Each time generating a new candidate solution, the previous foraging behaviors stored in memory are referred. Another enhanced search strategy is adopted in employed bee phase to avoid losing the balance between diversification and intensification. To evaluate the performance of FOABC, several groups of experiments are conducted on CEC 2017 benchmark problems. According to the experimental studies, FOABC is outstanding in comparisons with ABC variants and also performs re-markably well in practical applications, namely robot path planning.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Global optimization artificial bee colony algorithm Fractional calculus L?vy distribution Robot path planning

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Modified artificial bee colony algorithm for Multiple-Choice Multidimensional Knapsack Problem

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IEEE ACCESS 2023年 11卷 45255-45269页

作者： Mkaouar, Arij Htiouech, Skander Chabchoub, Habib Univ Sfax OLID Lab ISGIS Sfax Tunisia Univ Jeddah CS & AI Dept Jeddah Saudi Arabia Al Ain Univ Coll Business Abu Dhabi U Arab Emirates

The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified artificial bee colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several state-of-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems.

关键词： artificial bee colony algorithm Approximation algorithms Heuristic algorithms Search problems Hamming distances Linear programming Convergence multiple-choice multidimensional knapsack problem hamming distance surrogate relaxation

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A Hybrid Discrete artificial bee colony algorithm for Imaging Satellite Mission Planning

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IEEE ACCESS 2023年 11卷 40006-40017页

作者： Yang, Yang Liu, Desheng Space Engn Univ Sci & Technol Complex Elect Syst Simulat Lab Beijing 101400 Peoples R China

Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.

关键词： Satellites Planning Heuristic algorithms Optimization Imaging artificial bee colony algorithm Search problems imaging satellite mission planning

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