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Study on two-stage uncertain programming based on uncertainty theory

JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 633-642页

作者： Zheng, Mingfa Yi, Yuan Wang, Zutong Chen, Jeng-Fung Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Air Force Engn Univ Mat Management & Safety Engn Coll Xian 710051 Peoples R China Feng Chia Univ Dept Ind Engn & Syst Management 100 Wenhwa Rd Taichung 40724 Taiwan

In this paper, based on uncertainty theory, we first present a new class of two-stage uncertain programming model and give its deterministic equivalent programming problem. Then some fundamental properties of the two-stage uncertain programming problem, including the convexity of feasible set as well as objective function, are investigated. In addition, a solution method by employing an efficiently heuristic algorithm, called artificial bee colony algorithm, is applied to solve the two-stage uncertain programming problem. Finally, some numerical examples are provided to illustrate the novel method introduced in this paper.

关键词： Uncertainty theory Two-stage uncertain programming Convexity artificial bee colony algorithm

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Yaw control of an unmanned aerial vehicle helicopter using linear active disturbance rejection control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2017年第6期231卷 427-435页

作者： Ding, Li Ma, Rui Wu, Hongtao Feng, Chun Li, Qilin Jiangsu Univ Technol Coll Mech Engn Changzhou 213001 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Jiangsu Peoples R China Changzhou Inst Technol Sch Mech & Vehicle Engn Changzhou Peoples R China

In this article, a yaw channel controller with linear active disturbance rejection control is proposed for high-performance attitude tracking of an unmanned helicopter. In this control scheme, the linear extended state observer serves as a compensator, which can effectively reject parametric uncertainties and external disturbances. In this case, the attitude tracking controller is designed according to the input-output relation of yaw model. As a controlled plant, the yaw dynamical model is first obtained through the system identification method. Subsequently, in order to optimize the linear active disturbance rejection control applied to yaw control, an artificial bee colony algorithm is introduced to solve the problem of parameter tuning. Finally, comparative studies are carried out. The results from simulation and flight experiment show that the proposed controller provides better performances than the traditional controller.

关键词： Yaw control linear active disturbance rejection control unmanned helicopter system identification parameter tuning artificial bee colony algorithm

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Design of multiplierless prototype filter for two-channel filter bank using hybrid method in FCSD space

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IET CIRCUITS DEVICES & SYSTEMS 2017年第1期11卷 29-40页

作者： Sharma, Ila Kumar, Anil Singh, Girish Kumar Lee, Heung-No PDPM Indian Inst Informat Technol Design & Mfg Ja Jabalpur 482011 MP India Indian Inst Technol Roorkee Roorkee 247667 Uttrakhand India Gwangju Inst Sci & Technol GIST Korea Sch Elect Engn & Comp Sci EECS Gwangju 61005 South Korea

In this work, a hybrid method-based design of multiplierless two-channel filter bank has been proposed with a given stopband attenuation (A(s)) and roll-off factor. Windowing method has been used for efficient design of a prototype filter with novelty of exploiting quantised coefficients in canonical sign digit (CSD) and factorised canonical sign digit (FCSD) space by merging the concept of particle swarm optimisation and artificial bee colony algorithm. The quantised filter coefficients are optimised by varying cut-off frequency such that the magnitude response of prototype filter is approximately reduced to 0.707 at quadrature frequency. The implemented filter is synthesised using target field programmable gate arrays XC3S500E-4-FG320 on Xilinx Spartan 3E starter board. The performances of designed prototype filter is compared with the earlier published works in terms of reconstruction error, amplitude distortion, slices, flip-flops, four-input lookup tables and adders. The synthesis results demonstrate that the significant reduction in hardware is achieved in term of adder gain. For filter order, N = 32, and word length 12, the adder gain achieved in CSD and FCSD is 41.77 and 43.07%, respectively, while for N = 30, it is 35.44% in CSD and 36.70% in FCSD, respectively.

关键词： channel bank filters particle swarm optimisation ant colony optimisation circuit optimisation multiplierless prototype filter design FCSD space hybrid method-based design multiplierless two-channel filter bank stopband attenuation roll-off factor canonical sign digit space factorised canonical sign digit space particle swarm optimisation artificial bee colony algorithm quantised filter coefficients target field programmable gate arrays XC3S500E-4-FG320 Xilinx Spartan 3E starter board windowing method

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Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年 106卷 115-136页

作者： Ng, K. K. H. Lee, C. K. M. Chan, Felix T. S. Qin, Yichen Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

This study considers the aircraft sequencing and scheduling problem under the uncertainty of arrival and departure delays for multiple heterogeneous mixed-mode parallel runways. To enhance runway resilience, runway operations should remain robust to mitigate the effects of delay propagation. The main objective of this research was to identify an optimal schedule by evaluating the robustness of feasible solutions under its respective worst-case scenario. A novel artificial bee colony algorithm was developed and verified by experimental results. The proposed efficient artificial bee colony algorithm can obtain close-to optimal results with less computational effort in regard to a one-hour flight traffic planning horizon. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Robust scheduling Min-max regret approach Mixed-mode parallel runways Swarm intelligence artificial bee colony algorithm

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Parameter identification of concrete dams using swarm intelligence algorithm

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ENGINEERING COMPUTATIONS 2017年第7期34卷 2358-2378页

作者： Dou, Siqi Li, Junjie Kang, Fei Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian Peoples R China Tibet Univ Inst Technol Lhasa Peoples R China

Purpose - Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification. Design/methodology/approach - Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young's modulus of a concrete gravity dam and a concrete arch dam. Findings - Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency. Originality/value - Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.

关键词： Particle swarm optimization Inverse analysis Parameter identification artificial bee colony algorithm Concrete dam Fireworks algorithm

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A Cooperative Co-Evolutionary algorithm for Large-Scale Process Planning With Energy Consideration

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2017年第6期139卷 061016页

作者： Tao, Fei Bi, Luning Zuo, Ying Nee, A. Y. C. Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117576 Singapore

Process planning can be an effective way to improve the energy efficiency of production processes. Aimed at reducing both energy consumption and processing time (PT), a comprehensive approach that considers feature sequencing, process selection, and physical resources allocation simultaneously is established in this paper. As the number of decision variables increase, process planning becomes a large-scale problem, and it is difficult to be addressed by simply employing a regular meta-heuristic algorithm. A cooperative co-evolutionary algorithm, which hybridizes the artificial bee colony algorithm (ABCA) and Tabu search (TS), is therefore proposed. In addition, in the proposed algorithm, a novel representation method is designed to generate feasible process plans under complex precedence. Compared with some widely used algorithms, the proposed algorithm is proven to have a good performance for handling large-scale process planning in terms of maximizing energy efficiency and production times.

关键词： large-scale process planning energy consumption optimization artificial bee colony algorithm

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DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 74卷 132-141页

作者： Jiang, Jianhua Feng, Yunzhao Zhao, Jia Li, Keqin Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun Peoples R China Changchun Univ Technol Sch Comp Sci & Engn Changchun Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Live Virtual Machine (VM) consolidation is an effective method of improving energy-efficiency level in green data centers. Currently, to evaluate energy consumption in green data centers, energy-efficiency evaluation model with CPU utilization rate has been proposed. However, it is not suitable for data intensive computing due to great energy consumption by GPU-intensive processing. In this paper, we have proposed a new energy evaluation model with CPU and GPU utilization rates. There are two kinds of policies in live VM consolidation: one for VM selection and the other for VM allocation. Some researchers have proposed their solutions based on VM selection policy or VM allocation policy respectively. However, it will be a better energy-efficiency VM consolidation policy if these two polices are integrated together. Based on these two policies, a fast artificial bee colony (ABC) based energy efficiency live VM consolidation policy with data-intensive energy model, named as DataABC, is proposed. DataABC adopts the idea of artificial bee colony algorithm to get a fast and global optimized decision of VM consolidation. Compared with two state-of-art policies of PS-ABC and PS-ES, the total energy consumption of DataABC evidently drop by 9.72% and 5.84% respectively. As a result, based on the ESV metric, the DataABC approach has proved that (a) the energy-efficiency evaluation model with data intensive computing is valid and that (b) DataABC can save energy with a good Quality of Service (QoS) in green data centers. (C) 2016 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Energy-efficiency Green data center Virtual machine consolidation Virtual machine migration

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Optimal design and analysis of mechanical draft cooling tower using improved Jaya algorithm

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2017年 82卷 312-324页

作者： Rao, R. V. More, K. C. BITS Pilani Dept Mech Engn Dubai Campus Dubai 345055 U Arab Emirates SV Natl Inst Technol Dept Mech Engn Surat 395007 India

A cooling tower is an imperative component of industrial plants. The minimization of energy related expenses is critical for conservation of resources and energy savings. Hence, the present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of cooling tower from economic facets. In this work, six different examples are considered in the design optimization of mechanical draft cooling tower. Various researchers have attempted the same mathematical models by using different methods like Merkel method, Poppe method and artificial bee colony (ABC) algorithm. The results achieved by using the proposed self-adaptive Jaya algorithm are compared with the results achieved by using the Merkel method, Poppe method, ABC algorithm and basic Jaya algorithm. The proposed self-adaptive Jaya algorithm determines the population size automatically and the user need not tune the population size. The proposed self-adaptive Jaya algorithm is proved better as compared to the other optimization methods with respect to achieving the optimal value of the objective function at less computational effort. (C) 2017 Elsevier Ltd and IIR. All rights reserved.

关键词： Cooling tower Cost optimization Merkel method Poppe method artificial bee colony algorithm Jaya algorithm Self-adaptive Jaya algorithm

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A hybrid algorithm for a vehicle routing problem with realistic constraints

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INFORMATION SCIENCES 2017年 394卷 167-182页

作者： Zhang, Defu Cai, Sifan Ye, Furong Si, Yain-Whar Trung Thanh Nguyen Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Liverpool John Moores Univ Dept Maritime & Mech Engn Liverpool Merseyside England

Proliferation of multi-national corporations and extremely competitive business environments have led to an unprecedented demand for third-party logistics services. However, recent studies on the vehicle routing problem (VRP) have considered only simple constraints. They also do not scale well to real-world problems that are encountered in the logistics industry. In this paper, we introduce a novel vehicle routing problem with time window and pallet loading constraints;this problem accounts for the actual needs of businesses in the logistics industry such as the delivery of consumer goods and agricultural products. To solve this new VRP, we propose a hybrid approach by combining Tabu search and the artificial bee colony algorithm. A new benchmark data set is generated to verify the performance of the proposed algorithm because the proposed VRP has never been reported in the literature. Experiments are performed for a data set of Solomon's 56 vehicle routing problem with time windows. Our approach is superior to a number of other heuristic algorithms in a comparison on Solomon's VRPTW instances. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Vehicle routing problem Container loading Tabu search artificial bee colony algorithm

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Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers

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IET ELECTRIC POWER APPLICATIONS 2017年第8期11卷 1432-1440页

作者： Soued, Salah Ebrahim, Mohamed A. Ramadan, Haitham S. Becherif, Mohamed Bourgogne Franche Comte Univ UTBM Femto ST UMR CNRS 6174 Dept EnergyFCLabFR CNRS 3539 Belfort France Benha Univ Elect Engn Dept Fac Engn Shoubra Cairo Egypt Zagazig Univ Fac Engn Zagazig 44519 Egypt

Wind energy (WE) as an environment-friendly resource is green, clean, and innovative solution for the globe energy dilemma. For the blade pitch control (BPC) of the WE, the gain scheduling of the BPC becomes important to cope with the wind intermittency. The innovative idea of this study is to employ the novel advanced MOTs for enhancing the dynamic behaviour of the wind power plant. For this purpose, the genetic algorithm (GA), artificial bee colony (ABC) algorithm and grey Wolf optimiser (GWO) are used for the optimal tuning of the BPC system parameters. A comprehensive comparative study is presented to verify the effectiveness of the proposed MOTs over different conventional methods such as the conventional Zeigler-Nichols, and the simplex algorithm). This comparative analysis is based on the time response specifications such as maximum overshoot, settling time and steady-state error. The proportional-integral-differential (PID) controller is applied to the multivariable BPC for a wind turbine generating system connected to a large power system. The simulation results show that the GWO-PID is more effective than the ABC, GA and the conventional methods. Moreover,the GWO-PID controller robustness is verified in the presence of system parameter variations, an abrupt change in the mechanical torque and the wind speed variation.

关键词： wind turbines wind power wind power plants power generation scheduling power generation control blades pitch control (position) three-term control optimal control genetic algorithms multivariable control systems velocity control torque control optimal blade pitch control dynamic performance enhancement wind power plants metaheuristic optimisers wind energy natural energy resource continuous atmospheric condition variations gain scheduling wind intermittency optimal metaheuristic optimisation techniques MOT genetic algorithm GA artificial bee colony algorithm ABC grey Wolf optimiser GWO optimal BPC system parameter tuning time response specifications maximum overshoot settling time steady-state error proportional-integral-differential controller multivariable BPC wind turbine generating system GWO-PID controller robustness mechanical torque wind speed variation numerical simulations

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