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Advances in Manufacturing 2019年第2期7卷 238-247页

作者： Dong-Dong Chen Yong-Cheng Lin Xiao-Min Chen School of Mechanical and Electrical Engineering Central South University Changsha 410083 People's Republic of China State Key Laboratory of High Performance Complex Manufacturing Changsha 410083 People's Republic of China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha 410114 People's Republic of China

In this study, a strategy based on the particle swarm optimization (PSO) algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, namely, material models, optimality criterions, and a PSO algo rithm. The material models are utilized to predict microstructure information, such as recrystallization volume fraction and average grain size. The optimality crite? non can be determined by the designed target microstructures and random errors. The developed strategy is resolved using the PSO algorithm, which is an intelligent optimal algorithm. This algorithm does not need a derivable objective function, which renders it suitable for dealing with the complex hot forging process of alloy components. The optimal processing parameters (deformation temperature and strain rate) are obtained by the developed strategy and validated by the hot forging experiments. Uniform and fine target microstructures can be obtained using the optimized processing parameters, which indicates that the developed strategy is effective for controlling the microstructural evolution during the hot forging of the studied superalloy.

关键词： Processing parameters Microstructure Particle swarm optimization (PSO) algorithm Superalloy

swarm intelligence for classification of remote sensing data

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Science China Earth Sciences 2008年第1期51卷 79-87页

作者： Liu XiaoPing Li Xia Peng XiaoJuan Li HaiBo He JinQiang School of Geography and Planning Sun Yat-sen University Guangzhou 510275 China South China Sea Environment Monitor Center Guangzhou 510300 China

This paper proposes a new method to classify remote sensing data by using Particle swarm Optimization (PSO). This method is to generate classification rules through simulating the behaviors of bird flocking. Optimized... 详细信息

关键词： swarm intelligence particle swarm optimization (PSO) remote sensing

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle swarm Optimization Based Novel Neural Network

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1365-1383页

作者： Pratik Roy Ghanshaym Singha Mahapatra Kashi Nath Dey Department of Computer Science and Engineering University of CalcuttaKolkata-700106India Department of Mathematics National Institute of TechnologyPuducherryKaraikal-609609India

This paper proposes an artificial neural network(ANN) based software reliability model trained by novel particle swarm optimization(PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.

关键词： Artificial neural network(ANN) fuzzy particle swarm optimization(PSO) reliability prediction software reliability

Multi-Objective Particle swarm Optimization(MOPSO) for a Distributed Energy System Integrated with Energy Storage

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Journal of Thermal Science 2019年第6期28卷 1221-1235页

作者： ZHANG Jian CHO Heejin MAGO Pedro J. ZHANG Hongguang YANG Fubin Mechanical Engineering Department Mississippi State UniversityMississippi StateMS39762USA College of Environmental and Energy Engineering Beijing University of TechnologyBeijing 100124China

Distributed energy systems are considered as a promising technology for sustainable development and have become a popular research topic in the areas of building energy systems. This work presents a case study of optimizing an integrated distributed energy system consisting of combined heat and power(CHP), photovoltaics(PV), and electric and/or thermal energy storage for a hospital and large hotel buildings located in Texas and California. First, simulation models for all subsystems, which are developed individually, are integrated together according to a control strategy designed to satisfy both the electric and thermal energy requirements of a building. Subsequently, a multi-objective particle swarm optimization(MOPSO) is employed to obtain an optimal design of each subsystem. The objectives of the optimization are to minimize the simple payback period(PBP) and maximize the reduction of carbon dioxide emissions(RCDE). Finally, the energy performance for the selected building types and locations are analyzed after the optimization. Results indicate that the proposed optimization method could be applied to determine an optimal design of distributed energy systems, which reaches a trade-off between the economic and environmental performance for different buildings. With the presented distributed energy system, a peak shaving in electricity of about 300 kW and a reduction in boiler fuel consumption of 610 kW could be attained for the hospital building located in California for a winter day. For the summer and transition seasons, electricity peak shaving of 800 kW and 600 kW could be achieved, respectively.

关键词： multi-objective particle swarm optimization distributed energy system payback period carbon dioxide emission

A Context Sensitive Multilevel Thresholding Using swarm Based Algorithms

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1471-1486页

作者： Shreya Pare Anil Kumar Varun Bajaj Girish Kumar Singh PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005MPIndia Institute of Technology Roorkee Uttarakhand 247667India

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

关键词： Color image segmentation Kapur's entropy multilevel thresholding Otsu method swarm based optimization algorithms Tsalli's entropy

Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control

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Applied Mathematics and Mechanics(English Edition) 2019年第1期40卷 111-126页

作者： Zhaoyue XU Lin DU Haopeng WANG Zichen DENG School of Natural and Applied Science Northwestern Polytechnical University School of Aeronautics Northwestern Polytechnical University Ministry of Industry and Information Technology(MIIT) Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Department of Engineering Mechanics Northwestern Polytechnical University

Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of *** the above verify the immense potential applications of the PSO method in multibody system dynamics.

关键词： robotic dynamics multibody system symplectic method particle swarm optimization(PSO)algorithm instantaneous optimal control

Hybrid Particle swarm Optimization Algorithm Based on Entropy Theory for Solving DAR Scheduling Problem

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Tsinghua Science and Technology 2019年第3期24卷 281-290页

作者： Haowei Zhang Junwei Xie Jiaang Ge Junpeng Shi Zhaojian Zhang Air and Missile Defense College Air Force Engineering University National University of Defense Technology (NUDT) Air Force Early Warning Academy of PLA

An efficient task-scheduling algorithm in the Digital Array Radar(DAR) is essential to ensure that it can handle a large number of requested tasks simultaneously. As a solution to this problem, in this paper, we propose an optimization model for scheduling DAR tasks using a hybrid approach. The optimization model considers the internal task structure and the DAR task-scheduling characteristic. The hybrid approach integrates a particle swarm optimization algorithm with a genetic algorithm and a heuristic task-interleaving algorithm. We introduce the chaos theory to optimize initialized particles and use entropy theory to indicate the diversity of particles and adaptively adjust the inertia weight, the crossover probability, and the mutation probability. Then, we improve both the efficiency and global exploration ability of the hybrid algorithm. In the framework of the swarm exploration algorithm, we include a heuristic task-interleaving scheduling algorithm, which not only utilizes the wait interval to transmit or receive subtasks, but also overlaps the receive intervals of different tasks. In a large-scale simulation,we demonstrate that the proposed algorithm is more robust and effective than existing algorithms.

关键词： digital array radar task scheduling particle swarm optimization

A Review on swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 904-916页

作者： Kaizhou Gao Zhiguang Cao Le Zhang Zhenghua Chen Yuyan Han Quanke Pan IEEE the Macao Institute of Systems Engineering at Macao University of Science and Technology 999078 China School of Computer at Liaocheng Univeristy Liaocheng252059China the Department of Industrial Systems Engineering and Management National University of SingaporeSingapore Centre for Maritime Studies National University of SingaporeSingapore the Institute for Infocomm Research(I2R) the Agency for ScienceTechnology and Research(A*STAR)Singapore the School of Computer at Liaocheng Univeristy Liaocheng 252059China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China

Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many *** to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving *** than 60%of the publications are related to SI and *** paper intents to give a comprehensive literature review of SI and EA for solving ***,the mathematical model of FJSP is presented and the constraints in applications are ***,the encoding and decoding strategies for connecting the problem and algorithms are *** strategies for initializing algorithms?population and local search operators for improving convergence performance are ***,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are ***,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

关键词： Evolutionary algorithm flexible job shop scheduling review swarm intelligence

Surrogate-Assisted Particle swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 838-849页

作者： Zhiming Lv Linqing Wang Zhongyang Han Jun Zhao Wei Wang IEEE the Faculty of Electronics Information and Electrical Engineering Dalian University of Technology

For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.

关键词： Multiobjective optimization Pareto active learning particle swarm optimization (PSO) surrogate

Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle swarm Optimization

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Chinese Journal of Mechanical Engineering 2019年第1期32卷 65-78页

作者： Panpan Wang Liping Shi Yong Zhang Yifan Wang Li Han School of Electrical and Power Engineering China University of Mining & Technology

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.

关键词： Fault detection Broken rotor bars Induction motors Bare-bones particle swarm optimization Trust region