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Random Fuzzy Chance-constrained Programming Based on Adaptive Chaos Quantum Honey Bee Algorithm and Robustness Analysis 认领 引用 被引量：5
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作者 Han Xue Xun Li Hong-Xu Ma 《International Journal of Automation and computing》 2010年第1期115-122,共8页
This paper proposes an adaptive chaos quantum honey bee algorithm(CQHBA)for solving chance-constrained program-ming in random fuzzy environment based on random fuzzy simulations.Random fuzzy simulation is designed to ... This paper proposes an adaptive chaos quantum honey bee algorithm(CQHBA)for solving chance-constrained program-ming in random fuzzy environment based on random fuzzy simulations.Random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable.In CQHBA,each bee carries a group of quantum bits representing a solution.Chaos optimization searches space around the selected best-so-far food source.In the marriage process,random interferential discrete quantum crossover is done between selected drones and the queen.Gaussian quantum mutation is used to keep the diversity of whole population.New methods of computing quantum rotation angles are designed based on grads.A proof of con-vergence for CQHBA is developed and a theoretical analysis of the computational overhead for the algorithm is presented.Numerical examples are presented to demonstrate its superiority in robustness and stability,efficiency of computational complexity,success rate,and accuracy of solution quality.CQHBA is manifested to be highly robust under various conditions and capable of handling most random fuzzy programmings with any parameter settings,variable initializations,system tolerance and confidence level,perturbations,and noises. 展开更多
关键词 Honey bee algorithm random fuzzy programming quantum computation chaos optimization robustness
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Design of high phase-sensitivity BlueP/TMDC heterostructure-based SPR biosensor using improved artificial bee colony algorithm 认领 引用
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作者 Chong Yue Mantong Chen +1 位作者 Yaopu Lang Qinggang Liu 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第2期113-122,共10页
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu... This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphoreneransition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×106 °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures. 展开更多
关键词 SPR Phase modulation Sensitivity Improved artificial bee colony algorithm BlueP/TMDC Heterostructure
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Multi-Objective Structural Optimization of Composite Wind Turbine Blade Using a Novel Hybrid Approach of Artificial Bee Colony Algorithm Based on the Stochastic Method 认领 引用 被引量：1
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作者 Ramazan Özkan Mustafa Serdar Genç Ìlker Kayali 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3349-3380,共32页
The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallsca... The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallscale turbine blades using the Artificial Bee Colony(ABC)Algorithm based on the stochastic method to optimize both mass and cost(objective functions).The study used computational fluid dynamics(CFD)and structural analysis to consider the fluid-structure interaction.The optimization algorithm defined several variables:structural constraints,the type of composite material,and the number of composite layers to form a mathematical model.The numerical modeling was performed using the Ansys Fluent software and its Fluid-Structure Interaction(FSI)module.The ANSYS Composite PrePost(ACP)advanced composite modeling method was utilized in the structural design of composite materials.This study showed that the structurally optimized small-scale turbine blades provided a sustainable solution with improved efficiency compared to traditional designs.Furthermore,using CFD,structural analysis,and material characterization techniques first considered in this study highlights the importance of considering structural behavior when optimizing turbine blade designs. 展开更多
关键词 Turbine blade modeling structural optimization composite artificial bee colony algorithm
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Lake Eutrophic Evaluation Based on Bee Immune Evolutionary Algorithm 认领 引用 被引量：4
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作者 党媛 李祚泳 邹艳玲 《Agricultural Science & Technology》 CAS 2010年第4期156-158,188,共3页
In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluat... In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model. 展开更多
关键词 Lake Eutrophic evaluation Bee algorithm Bee immune evolutionary algorithm Parameter optimization
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Identification of a dynamic model for shape memory alloy actuator using Hammerstein-Wiener gray box and mutable smart bee algorithm 认领 引用 被引量：3
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作者 Alireza Fathi Ahmad Mozaffari 《International Journal of Intelligent Computing and Cybernetics》 EI 2013年第4期328-357,共30页
Purpose–The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy(SMA)actuators,as one of the most applic... Purpose–The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy(SMA)actuators,as one of the most applicable types of actuators in engineering and industry.The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators.Design/methodology/approach–To address the requirements of designing a pragmatic identification system,the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion.First,the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model.At the next step,they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier.Findings–Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator.Through different experiments,the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators.Furthermore,by extending the conclusions and expounding the obtained results,one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions.Based on the exerted experiments and implementing the method,the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks.Additionally,the derived gray box method has a higher degree of preference over the black box identifiers,as it allows a manipulated expert to extract the knowledge of the system at hand.Originality/value–The originality of the research paper is twofold.From the practical(engineering)point of view,the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model.From the computational point of view,the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand,and high computational expense pertinent to the structure of black-box identifiers. 展开更多
关键词 Hysteresis modeling Mutable smart bee algorithm Shape memory alloy actuators System identification
Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation 认领 引用 被引量：18
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作者 Zhicheng Zhang Jun Lin Yaowu Shi 《Journal of Bionic Engineering》 SCIE EI CSCD 2013年第1期100-109,共10页
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 ... 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. 展开更多
关键词 DOA estimation maximum likelihood artificial bee colony algorithm bio-inspired computing
Discrete Artificial Bee Colony Algorithm for Lot-streaming Flowshop with Total Flowtime Minimization 认领 引用 被引量：10
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作者 SANG Hongyan GAO Liang PAN Quanke 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2012年第5期990-1000,共11页
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 numb... 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. 展开更多
关键词 lot-streaming flowshop scheduling artificial bee colony algorithm total flowtime
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A Discrete Artificial Bee Colony Algorithm for Minimizing the Total Flow Time in the Blocking Flow Shop Scheduling 认领 引用 被引量：11
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作者 邓冠龙 徐震浩 顾幸生 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1067-1073,共7页
A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Se... A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion. 展开更多
关键词 blocking flow shop scheduling artificial bee colony algorithm total flow time
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An improved bearing fault detection strategy based on artificial bee colony algorithm 认领 引用 被引量：3
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作者 Haiquan Wang Wenxuan Yue +6 位作者 Shengjun Wen Xiaobin Xu Hans-Dietrich Haasis Menghao Su Ping liu Shanshan Zhang Panpan Du 《CAAI Transactions on Intelligence Technology》 SCIE EI 2022年第4期570-581,共12页
The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very crit... The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy. 展开更多
关键词 fault diagnosis feature extraction improved artificial bee colony algorithm improved one-dimensional ternary pattern method shapelet transformation
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Improved artificial bee colony algorithm for pressure source parameter inversion of Sakurajima volcano from InSAR data 认领 引用 被引量：3
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作者 Leyang Wang Linghui Xie Can Xi 《Geodesy and Geodynamics》 EI CSCD 2024年第6期635-641,共7页
A novel artificial bee colony algorithm was introduced for the eruption event of the Sakurajima volcano on August 9,2020,to invert the magma source characteristics below the volcano based on the point source Mogi mode... A novel artificial bee colony algorithm was introduced for the eruption event of the Sakurajima volcano on August 9,2020,to invert the magma source characteristics below the volcano based on the point source Mogi model.Considering that the Sakurajima volcano is surrounded by sea,all the deformation data are used to obtain the location and magma eruption volume of the volcano.In response to the weak local search capability of the artificial swarm algorithm,the difference between the global optimal individual and the un-roulette screened individual is introduced as the variance component in the onlooker stage.Detailed simulation experiments verify the improvement of the algorithm in terms of convergence speed.In real experiments,the Sakurajima volcano inversion shows closer fitting results and smaller residuals compared to the existing literature.Meanwhile,the convergence speed of the algorithm echoes with the simulation experiments. 展开更多
关键词 Sakurajima volcano D-InSAR Mogi model Artificial bee colony algorithm
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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 认领 引用 被引量：5
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作者 Leyang Wang Shuhao Han 《Geodesy and Geodynamics》 EI CSCD 2023年第3期244-253,共10页
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 impr... 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 model.The 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 transformation.The 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 derivation.The 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. 展开更多
关键词 Mixed additive and multiplicative random error Parameter estimation Accuracy evaluation Artificial bee colony algorithm Bootstrap method
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A Discrete Multi‑Objective Artificial Bee Colony Algorithm for a Real‑World Electronic Device Testing Machine Allocation Problem 认领 引用 被引量：2
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作者 Jin Xie Xinyu Li Liang Gao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第6期136-150,共15页
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 life.Users have higher quality requirements for... 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 life.Users have higher quality requirements for electronic devices.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 market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This 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 allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,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,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The 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 modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines. 展开更多
关键词 Electronic device Machine allocation Multi-objective optimization Artificial bee colony algorithm
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An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers 认领 引用 被引量：6
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作者 张素君 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第9期3471-3484,共14页
An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti... An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value. 展开更多
关键词 discrete artificial bee colony algorithm flow shop scheduling problem with intermediate buffers destruction and construction tournament selection
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Bio-Inspired Binary Bees Algorithm for a Two-Level Distribution Optimisation Problem 认领 引用 被引量：1
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作者 Shuo Xu Ze Ji +1 位作者 Duc Troung Pham Fan Yu 《Journal of Bionic Engineering》 SCIE EI 2010年第2期161-167,共7页
Two uncoupleable distributions,assigning missions to robots and allocating robots to home stations,accompany the use ofmobile service robots in hospitals.In the given problem,two workload-related objectives and five g... Two uncoupleable distributions,assigning missions to robots and allocating robots to home stations,accompany the use ofmobile service robots in hospitals.In the given problem,two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm(BBA)is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem,in which constraint handling technique(Multiobjective Transformation,MOT),multiobjectiveevaluation method(nondominance selection),global search strategy(stochastic search in the variable space),local searchstrategy(Hamming neighbourhood exploitation),and post-processing means(feasibility selection)are the main issues.TheBBA is then demonstrated with a case study,presenting the execution process of the algorithm,and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes. 展开更多
关键词 Binary Bees Algorithm bioinspiration two-level distribution combinatorial optimisation multiobjectives multi-constraints
Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm 认领 引用 被引量：6
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作者 Guo Jiansheng Wang Zutong +1 位作者 Zheng Mingfa Wang Ying 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2014年第6期1477-1487,共11页
Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coeffici... Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach. 展开更多
关键词 Artificial bee colony algorithm Multiobjective optimization Redundancy allocation problem Repairable systems Uncertainty theory
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Improved artificial bee colony algorithm with mutual learning 认领 引用 被引量：10
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作者 Yu Liu Xiaoxi Ling +1 位作者 Yu Liang Guanghao Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第2期265-275,共11页
The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs ... The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning （MutualABC） that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments. 展开更多
关键词 artificial bee colony （ABC） algorithm numerical func- tion optimization swarm intelligence mutual learning.
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Optimization and Parameters Estimation in Ultrasonic Echo Problems Using Modified Artificial Bee Colony Algorithm 认领 引用 被引量：1
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作者 Jinghua Zhou Xiaofeng Zhang +1 位作者 Guangbin Zhang Dongmei Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2015年第1期160-169,共10页
The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape,size,and orientation of the reflectors as well as the microstructure of the propagation path.Accurate es... The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape,size,and orientation of the reflectors as well as the microstructure of the propagation path.Accurate estimation of the ultrasonic echo pattern is essential in determining the object or propagation path properties.This paper proposes a parameter estimation method for ultrasonic echoes based on Artificial Bee Colony(ABC)algorithm which is one of the most recent swarm intelligence based algorithms.A modified ABC(MABC)algorithm is given by adding an adjusting factor to the neighborhood search formula of traditional ABC algorithm in order to enhance its performance.The algorithm could overcome the impact of different search range on estimation accuracy to solve the multi-dimensional parameter optimization problems.The performance of the MABC algorithm is demonstrated by numerical simulation and ultrasonic detection experiments.Results show that MABC not only can accurately estimate various parameters of the ultrasonic echoes,but also can achieve the optimal solution in the global scope.The proposed algorithm also has the advantages of fast convergence speed,short running time and real-time parameters esti-mation. 展开更多
关键词 artificial bee colony algorithm swarm intelligence global optimization ultrasonic echoes ultrasonic testing
Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing 认领 引用 被引量：1
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作者 Shasha Zhao Huanwen Yan +3 位作者 Qifeng Lin Xiangnan Feng He Chen Dengyin Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第1期1135-1156,共22页
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall... Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental. 展开更多
关键词 Cloud computing distributed processing evolutionary artificial bee colony algorithm hierarchical particle swarm optimization load balancing
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Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speed, Part Ⅰ–Aerodynamic optimization using genetic, bee colony and gradient descent algorithms 认领 引用 被引量：16
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作者 Andreea Koreanschi Oliviu Sugar Gabor +5 位作者 Joran Acotto Guillaume Brianchon Gregoire Portier Ruxandra Mihaela Botez Mahmoud Mamou Youssef Mebarki 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第1期149-163,共15页
In this paper, an ‘in-house＇ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm＇s ... In this paper, an ‘in-house＇ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm＇s performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods,namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated. 展开更多
关键词 Artificial bee colony Airfoil optimization Genetic algorithm Morphing wing Optimization
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