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Optimal allocation of regional water resources based on simulated annealing particle swarm optimization algorithm

ENERGY REPORTS 2022年 8卷 9119-9126页

作者： Wang, Zhanping Tian, Juncang Feng, Kepeng Ningxia Univ Coll Civil & Hydraul Engn Yinchuan 750021 Peoples R China Ningxia Univ Sch Math & Stat Yinchuan 750021 Peoples R China Ningxia Res Ctr Technol Water Saving Irrigat & Wat Yinchuan 750021 Peoples R China Engn Res Ctr Efficient Utilizat Water Resources Mo Yinchuan 750021 Peoples R China

Given the fast growth of economy in Ningxia, China, contradictions between the increase of water resources and the decrease of water supply become increasingly prominent. Therefore, it is critical to utilize limited water resources in a rational manner. This study uses the multi-objective programming theory and forms a multi-objective optimum allocation model for the purpose of using regional water resources sustainably. By aiming at maximizing the comprehensive benefits of society, the model solves the problem that particles are prone to be trapped into local minima by introducing the idea of simulated annealing into the basic particle swarm optimization (PSO) algorithm. The simulated annealing (SA) particle swarm algorithm is applied to solve the model and obtain the optimum allocation schemes of water resources at three different precipitation frequencies in the planning year of Yinchuan (2025), the capital city of Ningxia, China. With this, the model provides a scientific basis for the management of water resources in the city. The results indicate that the model is built upon a scientific and practical foundation, and the algorithm has practical significances. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Water resources Optimal allocation Simulated annealing particle swarm algorithm

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Synthesis of circular antenna array using crossed particle swarm optimization algorithm

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2006年第13期20卷 1785-1795页

作者： Chen, T. B. Dong, Y. L. Jiao, Y. C. Zhang, F. S. Xidian Univ Natl Lab Antennas & Microwave Technol Xian 710071 Peoples R China

The particle swarm optimization (PSO) algorithm presents a new way for finding an optimal solution of complex optimization problems. In this paper a modified particle swarm optimization algorithm is applied to the optimization of the antenna array. We modify the PSO algorithm in some aspects. Firstly, the dynamic parameter is introduced to update the position equation, and the particles are limited in the search region. A new strategy for updating the speed is then adopted, in which the speed is weakened linearly. Based on these strategies, we proposed a new PSO algorithm named the crossed PSO algorithm. Simulation results show that the optimal pattern of the antenna array is able to approach the desired pattern. The results also demonstrate that the modified algorithm is superior to the original algorithm and the nonlinear least-square method.

关键词： synthesis optimal solution particle swarm optimization algorithms Antenna arrays Loop antennas dynamic parameter algorithm simulation parametric subharmonic oscillator particle swarm algorithm speed

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Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

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NEUROCOMPUTING 2015年 158卷 109-116页

作者： Jiang, Shujuan Shi, Jiaojiao Zhang, Yanmei Han, Han China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China

Software testing aims to search a set of test data in the entire search space to satisfy a certain standard of coverage. Therefore, finding an effective approach for automatic test data generation is a key issue of software testing. This paper proposes a new approach of reduced adaptive particle swarm optimization for generating the test data automatically. First, the approach reduces the particle swarm evolution equations and gets an evolution equation without velocity. Then, the approach makes an adaptive adjustment scheme based on inertia weight for the reduced evolution equation, which is different from the methods that directly act on the particle velocity in the past. The approach directly impacts on the particle position, namely actual problem solution. Next, according to the particle fitness and the particle aggregation degree, the population will be divided into three parts and inertia weight of each part will be designed accordingly. This can balance the search capabilities of algorithm between global and local. Finally, the approach is applied to automatic test data generation. The experiments results show that our approach can enhance convergence speed of algorithm and solve the problems that particle swarm algorithm easily falls into the local optimal solution and has low search accuracy. The experiments results also turn out that our approach can improve the efficiency of generating test data automatically. (C) 2015 Elsevier B.V. All rights reserved.

关键词： particle swarm algorithm Test data generation Inertia weight Adaptive adjustment scheme particle aggregation degree

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Supply chain scheduling optimization based on genetic particle swarm optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14767-14775页

作者： Xiong, Feng Gong, Peisong Jin, P. Fan, J. F. Zhongnan Univ Econ & Law Sch Business Adm Wuhan 430073 Hubei Peoples R China Zhongnan Univ Econ & Law Inst Operat Management & Syst Engn IOPSE Wuhan 430073 Hubei Peoples R China Zhongnan Univ Econ & Law Wuhan 430073 Hubei Peoples R China Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Hubei Peoples R China

In order to optimize supply chain scheduling problem in mass customization mode, the mathematical programming of supply chain scheduling optimization problem is modelled. At the same time, model mapping is defined as a directed graph to facilitate the application of intelligent search algorithms. In addition, the features of genetic algorithm and particle swarm algorithm are introduced. Genetic algorithm has a strong global search capability, and particle swarm optimization algorithm has fast convergence speed. Therefore, the two algorithms are combined to construct a hybrid algorithm. Finally, the hybrid algorithm is used to solve the supply chain optimization scheduling problem model. Compared with other algorithms, the results show that the hybrid algorithm has better performance. The mathematical programming model used in this paper can be further extended and improved.

关键词： Scheduling optimization Hybrid algorithm Genetic algorithm particle swarm algorithm

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Determining the Optimum Cross-sectional Area of the Medium Voltage Feeder for Loss Reduction and Voltage Profile Improvement based on particle swarm algorithm

Determining the Optimum Cross-sectional Area of the Medium V...

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International Conference on Environment and Electrical Engineering

作者： F. Hashemi A. R. Alizade S. J. Zebardast N. Ghadimi Islamic Azad University Science And Research branch-Tehran Ardabil Province Electricity Distribution co Khorramshahr Combined Cycle Power Plant Islamic Azad University

ISBN: (纸本)9781424487790

In this paper, the optimal cross-sectional area of medium voltage feeder will be designed based on particle swarm optimization (PSO) so as to reduce power and energy losses and improve voltage profile in distribution system. To analyze the network status in any step, DC power flow is utilized which is done robustly and with high convergence rate of the system. In the proposed method, medium voltage feeders' cross-sectional areas are calculated regarding not only economic factors and power loss costs, but also constraints such as current carrying capacity of feeder and maximum permissible voltage drop. Since particle swarm algorithm is used as the optimization tool, with high possibility, obtained solution would be near the absolute optimum point. This method was implemented in MATLAB7 environment. Numerical simulation results and comparison with different references, shows the proposed method capability in desired problem solving.

关键词： Determining cross-sectional area of the medium voltage feeder particle swarm algorithm Loss reduction and voltage profile improvement

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Niche particle swarm algorithm and Application Study

Niche Particle Swarm Algorithm and Application Study

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International Conference on Broadcast Technology and Multimedia Communication(BTMC 2010)

作者： Tang Chao-li Institute of Electrical and Information Engineering Anhui University of Science and Technology Huainan,Anhui232001,China Huang You-rui,Li ji-yun Institute of Electrical and Information Engineering Anhui University of Science and Technology Huainan,Anhui232001,China

To the multi-modal function optimization problem,after analyzing characteristics and deficiencies of traditional niche genetic and niche clonal selection algorithms,it proposes a niche particle swarm algorithm(NPSA),which based on principle of particle swarm *** the convergence analysis and simulation experiments of four typical multi-modal functions,conclusions show that the NPSA has eximious simplicity and high efficiency.

关键词： multi-modal function optimization niche technology particle swarm algorithm

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Multi-objective Optimization Design for Gear Reducer Based on the Grey particle swarm algorithm

Multi-objective Optimization Design for Gear Reducer Based o...

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2012 2nd lnternational Conference on Materials Engineering for Advanced Technologies (ICMEAT 2012)

作者： An Feng Yuan Sicong Yan Weidong Wang Donghong XI'AN university of architecture and technology College of Mechanical and Electrical Engineering Taiyuan Iron and Steel Group Corporation Institute of Building Mechanization China Academy of Building Research

ISBN: (纸本)9783037855874

Combining the thought of correlation degree analysis in the theory of grey,use of particle swarm algorithm,seeking it's individual extreme value and global extreme value,and puts forward to the goal of mathematical model about more gray particle swarm optimization algorithm is presented,the algorithm is applied to speed reducer hoisting mechanism in the optimization of *** optimization results show that the optimal parameters,than the original design of parameters for satisfactory results show the particle swarm optimization algorithm is used for gray hoisting mechanism optimized parameter design of gear reducer is effective and feasible.

关键词： Grey theory particle swarm algorithm Optimize design Reducer

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A novel camera calibration technique based on differential evolution particle swarm optimization algorithm

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NEUROCOMPUTING 2016年第PartA期174卷 456-465页

作者： Deng, Li Lu, Gen Shao, Yuying Fei, Minrui Hu, Huosheng Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai 200025 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

Camera calibration is one of the fundamental issues in computer vision and aims at determining the intrinsic and exterior camera parameters by using image features and the corresponding 3D features. This paper proposes a relationship model for camera calibration in which the geometric parameter and the lens distortion effect of camera are taken into account in order to unify the world coordinate system (WCS), the camera coordinate system (CCS) and the image coordinate system (ICS). Differential evolution is combined with particle swarm optimization algorithm to calibrate the camera parameters effectively. Experimental results show that the proposed algorithm has a good optimization ability to avoid local optimum and can complete the visual identification tasks accurately. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Camera calibration Internal parameter External parameter Differential evolution particle swarm algorithm Visual identification

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Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2005年第9-10期25卷 964-971页

作者： Jerald, J Asokan, P Prabaharan, G Saravanan, R Deemed Univ SASTRA Sch Mech Engn Thanjavur 613402 India Reg Engn Coll Dept Prod Engn Tiruchirappalli 620015 India JJ Coll Engn & Technol Dept Mech Engn Tiruchirappalli 620009 India

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling;these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented.

关键词： flexible manufacturing system genetic algorithm memetic algorithm particle swarm algorithm scheduling simulated annealing

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Designing a mirrored Howland circuit with a particle swarm optimisation algorithm

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INTERNATIONAL JOURNAL OF ELECTRONICS 2016年第6期103卷 1029-1037页

作者： Bertemes-Filho, Pedro Negri, Lucas H. Vincence, Volney C. Univ Estado Santa Catarina Dept Elect Engn Santa Catarina Brazil

Electrical impedance spectroscopy usually requires a wide bandwidth current source with high output impedance. Non-idealities of the operational amplifier (op-amp) degrade its performance. This work presents a particle swarm algorithm for extracting the main AC characteristics of the op-amp used to design a mirrored modified Howland current source circuit which satisfies both the output current and the impedance spectra required. User specifications were accommodated. Both resistive and biological loads were used in the simulations. The results showed that the algorithm can correctly identify the open-loop gain and the input and output resistance of the op-amp which best fit the performance requirements of the circuit. It was also shown that the higher the open-loop gain corner frequency the higher the output impedance of the circuit. The algorithm could be a powerful tool for developing a desirable current source for different bioimpedance medical and clinical applications, such as cancer tissue characterisation and tissue cell measurements.

关键词： Electrical impedance spectroscopy bioimpedance numerical analysis Howland circuit particle swarm algorithm

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