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Parameter identification for Van Der Pol-Duffing oscillator by a novel artificial bee colony algorithm with differential evolution operators

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APPLIED MATHEMATICS AND COMPUTATION 2013年 222卷 132-144页

作者： Gao, Fei Lee, Xue-jing Fei, Feng-xia Tong, Heng-qing Qi, Yi-bo Deng, Yan-fang Balasingham, Ilangko Zhao, Hua-ling Wuhan Univ Technol Dept Math Sch Sci Wuhan 430070 Hubei Peoples R China Norwegian Univ Sci & Technol Signal Proc Grp Dept Elect & Telecommun N-7491 Trondheim Norway Wuhan Univ Technol Sch Sci Dept Stat Wuhan 430070 Hubei Peoples R China

This paper is concerned with the uncertain parameters' identification of Van Der Pol-Duffing oscillators (VDPD) through a non-Lyapunov way with ABC and ABCDEO respectively by converting the problem into a multiple modal non-negative functions' minimization, which is of vital significance and attracts increasing interests in various research fields. Firstly, a novel artificial bee colony (ABC) algorithm with differential evolution operators (ABCDEO) is introduced to accelerate ABC due to the slow nature of the collective intelligence of honey bee swarms, by doing differential evolution operators to the employed bees colony in a certain probability, a special region contraction rules, and swarm re-initialization strategy based on number-theory nets. Secondly, the unknown parameters of VDPD for three main physical states, the cases with noise, and a comprehensive set of twelve complex benchmark functions including a wide range of dimensions, are employed for experimental verification. Experimental results confirm that the ABCDEO is a successful method in VDPD's parameters' identification. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Parameters identification artificial bee colony algorithm Differential evolution operators Swarm re-initialization strategy Van Der Pol-Duffing oscillators Nonlinear optimization

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A new method for multicomponent mixture separation cascade optimization using artificial bee colony algorithm

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PROGRESS IN NUCLEAR ENERGY 2020年 124卷 103371-103371页

作者： Ezazi, Farzaneh Mallah, Mohammad Hassan Sabet, Javad Karimi Norouzi, Ali Atom Energy Org Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch POB 11365-8486 Tehran Iran Adv Technol Co Atom Energy Org POB 143995-5931 Tehran Iran

This research deals with a cascade design method using artificial bee colony algorithm to find multicomponent mixture separation cascade optimal parameters. Therefore, the real coded artificial bee colony was developed and the waste and product concentrations of a target component and the total interstage flow of the cascade were selected as the objective function main items. To evaluate the proposed method, a comparative study between the simulated and optimum matched-X cascades and optimum cascade has been performed. The method leads to an optimum matched-X cascade with lower total interstage flow than the common matched-X cascade, and it can successfully find the optimum cascade with better characteristics than the common and optimum matched-X cascades. Also, it was compared with the pre-published results of particle swarm optimization, in the case of variable separation factors. The results revealed that the method significantly outperformed the particle swarm optimization and usage of the algorithm guarantees finding admissible solution.

关键词： Matched-X cascade Objective function Total interstage flow artificial bee colony algorithm Particle swarm optimization

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Applicability of artificial bee colony algorithm for nurse scheduling problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2014年第Supp 1期7卷 121-136页

作者： Buyukozkan, Kadir Sarucan, Ahmet Karadeniz Tech Univ Dept Ind Engn TR-61080 Trabzon Turkey Selcuk Univ Dept Ind Engn TR-42000 Selcuklu Konya Turkey

This paper describes the first artificial bee colony (ABC) algorithm approach applied to nurse scheduling evaluated under different working environments. For this purpose, the model has been applied on a real hospital where data taken from different departments of the hospital were used and the schedules from the model were compared with the existing schedules. The results obtained indicated that the proposed model exhibits success in solving the nurse scheduling problems in hospitals.

关键词： artificial bee colony algorithm nurse listing nurse scheduling task assignment

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A new fuzzy random multi-objective portfolio model with different entropy measures using fuzzy programming based on artificial bee colony algorithm

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ENGINEERING COMPUTATIONS 2022年第2期39卷 627-649页

作者： Deng, Xue He, Xiaolei Huang, Cuirong South China Univ Technol Sch Math Guangzhou Peoples R China South China Univ Technol Sch Business Adm Guangzhou Peoples R China

Purpose This paper proposes a fuzzy random multi-objective portfolio model with different entropy measures and designs a hybrid algorithm to solve the proposed model. Design/methodology/approach Because random uncertainty and fuzzy uncertainty are often combined in a real-world setting, the security returns are considered as fuzzy random numbers. In the model, the authors also consider the effects of different entropy measures, including Yager's entropy, Shannon's entropy and min-max entropy. During the process of solving the model, the authors use a ranking method to convert the expected return into a crisp number. To find the optimal solution efficiently, a fuzzy programming technique based on artificial bee colony (ABC) algorithm is also proposed. Findings (1) The return of optimal portfolio increases while the level of investor risk aversion increases. (2) The difference of the investment weights of the optimal portfolio obtained with Yager's entropy are much smaller than that of the min-max entropy. (3) The performance of the ABC algorithm on solving the proposed model is superior than other intelligent algorithms such as the genetic algorithm, differential evolution and particle swarm optimization. Originality/value To the best of the authors' knowledge, no effect has been made to consider a fuzzy random portfolio model with different entropy measures. Thus, the novelty of the research is constructing a fuzzy random multi-objective portfolio model with different entropy measures and designing a hybrid fuzzy programming-ABC algorithm to solve the proposed model.

关键词： Liquidity Cardinality constraint Fuzzy random number Fuzzy programming artificial bee colony algorithm

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Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem

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INFORMATION SCIENCES 2017年 421卷 70-84页

作者： Zhong, Yiwen Lin, Juan Wang, Lijin Zhang, Hui Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Fujian Peoples R China Univ Louisville Dept Comp Engn & Comp Sci Louisville KY 40217 USA

artificial bee colony (ABC) algorithm, which has explicit strategies to balance intensification and diversification, is a smart swarm intelligence algorithm and was first proposed for continuous optimization problems. In this paper, a hybrid discrete ABC algorithm, which uses acceptance criterion of threshold accepting method, is proposed for Traveling Salesman Problem (TSP). A new solution updating equation, which can learn both from other bees and from features of problem at hand, is designed for the TSP. Aiming to enhance its ability to escape from premature convergence, employed bees and onlooker bees use threshold acceptance criterion to decide whether or not to accept newly produced solutions. Systematic experiments were performed to show the advantage of the new solution updating equation, to verify the necessity of using non-greedy acceptance strategy for keeping sufficient diversity, to compare different selection schemes for onlooker bees, and to analyze the contribution of scout bee. Comparison experiments performed on a wide range of benchmark TSP instances have shown that the proposed algorithm is better than other ABC-based algorithms and is better than or competitive with many other state-of-the-art algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Traveling salesman problem Threshold acceptance criterion Collective behavior Selection schemes

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Coupling Optimization Design of Aspirated Compressor Airfoil and Aspirated Scheme Based on artificial bee colony algorithm and CST Method

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2015年第1期32卷 25-39页

作者： Li, Jun Liu, Bo Zhao, Yan Yang, Xiaodong Lu, Xiaofeng Wang, Lei Northwestern Polytech Univ Sch Power & Energy Xian 710072 Shaanxi Peoples R China AVIC Shenyang Engine Design & Res Inst Shenyang Liaoning Peoples R China AVIC Aerodynam Res Inst Shenyang Liaoning Peoples R China

This paper focuses on creating a new design method optimizing both aspirated compressor airfoil and the aspiration scheme simultaneously. The optimization design method is based on the artificial bee colony algorithm and the CST method, while the flow field is computed by one 2D computational program. The optimization process of the rotor tip and stator tip airfoil from an aspirated fan stage is demonstrated to verify the effectiveness of the new coupling method. The results show that the total pressure losses of the optimized stator tip and rotor tip airfoil are reduced relatively by 54% and 20%, respectively. artificial bee colony algorithm and CST method indicate a satisfying applicability in aspirated airfoil optimization design. Finally, the features of aspirated airfoil designing process are concluded.

关键词： aspirated compressor airfoil aspirated scheme coupling optimization design artificial bee colony algorithm CST method

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A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

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INFORMATION SCIENCES 2016年 367卷 1012-1044页

作者： Cui, Laizhong Li, Genghui Lin, Qiuzhen Du, Zhihua Gao, Weifeng Chen, Jianyong Lu, Nan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Xidian Univ Sch Math & Stat Xian Peoples R China

Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tradeoff between exploration and exploitation, in this paper, we propose a depth-first search (DFS) framework. The key feature of the DFS framework is to allocate more computing resources to the food sources with better quality and easier to be improved for evolution. We apply the DFS framework to ABC, GABC and CABC, yielding DFSABC, DFSGABC and DFSCABC respectively. The experimental results on 22 benchmark functions show that the DFS framework can speed up convergence rate in most cases. To further improve the performance, we introduce two novel solution search equations: the first equation incorporates the information of elite solutions and can be applied to the employed bee phase, while the second equation not only exploits the information of the elite solutions but also employs the current best solution in the onlooker bee phase. Finally, two novel proposed search equations are combined with DFSABC to form a new variant of ABC, named DFSABC_elite. Through the comparison of DFSABC_elite with other variants of ABC and some non-ABC methods, the experimental results demonstrate that DFSABC_elite is significantly better than the compared algorithms on most of the test functions in terms of solution quality, robustness, and convergence speed. (C) 2016 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Depth-first search framework Computing resources allocation Novel search equations

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artificial bee colony algorithm with a pure crossover operation for binary optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 152卷 107011-107011页

作者： Xiang, Wan-li Li, Yin-zhen He, Rui-chun An, Mei-qing Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Inst Modern Logist Lanzhou 730070 Gansu Peoples R China

artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.

关键词： artificial bee colony algorithm Pure crossover operation Frequency of perturbation Opposition-based learning Facility location

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Differential artificial bee colony algorithm for Global Numerical Optimization

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JOURNAL OF COMPUTERS 2011年第5期6卷 841-848页

作者： Wu, Bin Qian, Cun Hua Nanjing Univ Technol Coll Management Sci & Engn Nanjing Peoples R China

artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. In order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions, a differential ABC (DABC) is proposed. The differential operator obeys uniform distribution and creates candidate food position that can fully represent the solution space. So the diversity of populations and capability of global search will be enhanced. To show the performance of our proposed DABC, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the DABC can effectively enhance the searching efficiency and greatly improve the searching quality.

关键词： artificial bee colony algorithm Global Numerical Optimization Differential Evolution

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DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期90卷 1085-1103页

作者： Zhou, Jiajun Yao, Xifan South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

Cloud manufacturing (CMfg) is a new type of service-oriented manufacturing paradigm, in which all kinds of manufacturing resources are encapsulated as manufacturing services (MSs) and can be invoked by customers on demand. Manufacturing service composition (MSC) is a key technology in CMfg for creating value-added services to complete complicated manufacturing tasks by aggregating qualified MSs together. However, current MSC approaches have some drawbacks and there still exist some issues remained to be solved: (1) large quantities of candidate services increase the complexity of service dynamic composition, which poses scalability concerns and on-demand efficient solutions;(2) the service domain features (e.g., service prior, correlation, and similarity) that have a strong influence on the efficiency of service composition are not considered adequately, which causes undesirable efficiency in practical service applications;and (3) dynamic characteristics of QoS (quality of service) values in an open network environment are not considered adequately. To effectively address such problems, this paper first proposes a context-aware artificial bee colony (caABC) algorithm based on the principle of ABC and service features in the cloud environment. Then the differential evolution-enhanced caABC, i.e., the so-called DE-caABC, is designed to increase the searching performance of ABC further. Additionally, dynamics of trust QoS is investigated with the introduction of time decay function. Finally, the feasibility and effectiveness of DE-caABC are validated through the experiments.

关键词： Cloud manufacturing Manufacturing service composition Service domain feature Quality of service Differential evolution Context awareness artificial bee colony algorithm

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