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World Automation Congress (WAC)

作者： Chen, Xichun Wang, Junli Lanzhou Jiaotong Univ Sch Traff & Transportat Engn Lanzhou 730070 Peoples R China Gansu Prov Supervis & Inspect Ctr Product Qual Lanzhou Peoples R China

ISBN: (纸本)9781467344975

The aim of logistics distribution center location is to improve the logistics service quality and reduce the transportation cost of goods. The problems are related to both the classical location problem and the vehicle routing problem. This paper proposes logistics distribution center location based on particle swarm optimization algorithm. It models the logistics distribution center location problem considering the user demand and operating cost, then it uses modified particle swarm optimization algorithm to give solution. Experimental result shows that the proposed model and algorithm is effective.

关键词： distribution center location particle swarm optimization algorithm optional point

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particle swarm optimization-Based Deep Neural Network for Digital Modulation Recognition

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IEEE ACCESS 2019年 7卷 104591-104600页

作者： Shi, Wenzhe Liu, Dejun Cheng, Xing Li, Yang Zhao, Yang China Univ Petr Coll Informat Sci & Engn Beijing 102249 Peoples R China

Modulation recognition is a major task in many wireless communication systems including cognitive radio and signal reconnaissance. The diversification of modulation schemes and the increased complexity of the channel environment put higher requirements on the correct identification of modulated signals. Deep learning (DL) is considered as a potential solution to solve these problems due to the superior big data processing and classification capabilities. This paper proposes an efficient digital modulation recognition method based on deep neural network (DNN) model. Furthermore, we present the particle swarm optimization (PSO) algorithm to optimize the number of hidden layer nodes of the DNN so as to solve the problem that the traditional DNN is trapped in local minimum values and the number of hidden layer nodes needs selecting manually. In this paper, we utilize the proposed PSO-DNN method to learn characteristics extracted from the modulated signal added by additive white Gaussian noise (AWGN) and to train the network, which can improve the performance of recognition under the condition of low signal-to-noise ratio (SNR). The experimental results demonstrate that the recognition rate on this algorithm has improved by 9.4% and 8.8% compared with methods that adopt conventional DNN and support vector machine (SVM) when SNR equals 0 and 1 dB, respectively. Besides, another experiment compared with the genetic algorithm (GA) also proves that our proposed algorithm is more effective in optimizing the DNN. The proposed method is easy to be implemented so that it has a broad development prospect in modulation recognition.

关键词： Additive white Gaussian noise deep neural network digital modulation recognition particle swarm optimization algorithm

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General metaheuristic-based methodology for computation and decomposition of LMPs

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ELECTRICAL ENGINEERING 2021年第2期103卷 793-811页

作者： Saraiva, Felipe Oliveira Silva Paucar, Vicente Leonardo Fed Univ Maranhao UFMA Dept Elect Engn BR-65085580 Sao Luis MA Brazil

Locational marginal prices (LMPs) are economic signals paramount to the deliberations, policy designs, and planning judgments of most electricity markets. This paper presents a general metaheuristic-based methodology applied in the scope of two subproblems to compute and decompose the LMPs considering the incorporation of a distributed slack bus model in the power flow formulation. The deterministic core of the methodology is governed by a process of sequential setting of maximum number of metaheuristic iterations based on load disharmony indices (LDIs) in the context of the initial subproblem. The metaheuristic crux of the methodology is anchored in a random initialization strategy based on time-traveling parameters in the environment of both subproblems. In the introductory arrangement of the optimization sequence, the chain order of the operating horizon periods is defined based on the LDIs. Due to its notorious merits and the breadth of its applications in power system problems, a particle swarm optimization (PSO) algorithm model is used to solve the mentioned subproblems. For the practical purposes of this paper, in the PSO algorithm model instance the particle coordinates are adopted as time-traveling metaheuristic parameters. Numerical simulations on a 3-bus system and on the IEEE 30-bus test system corroborate the efficiency and adequacy of the proposed methodology.

关键词： Electricity market LMP decomposition Load disharmony index Locational marginal price particle swarm optimization algorithm

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Techno-economic Unified OPF Modeling for VSC-HVDC Converter Installation

ELECTRICA

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ELECTRICA 2021年第3期21卷 352-+页

作者： Agrawal, Sunilkumar Kundu, Prasanta SV Natl Inst Technol Dept Elect Engn Surat India

This paper demonstrates the analysis of novel methodology developed to select optimal buses for installation of convertor stations in the hybrid voltage source converter based high voltage direct current system. Here, a modified unified optimal power flow model is developed for the optimal power flow problem and solved using the particle swarm optimization technique for the voltage source converter based high voltage direct current network. The analysis has been performed for optimizing the various techno-economic objective functions, including generation cost, voltage deviation, and total power system losses, for better power system operation. The developed unified optimal power flow model's effectiveness and methodology for deciding the high voltage direct current converter's optimal location are examined, with several tests performed with modified five bus and IEEE-30 bus system. The impact of high voltage direct current line replacement is decided based on optimal results obtained for selected techno-economic objective functions by replacing each AC line with high voltage direct current independently. The obtained results have proved the voltage source converter high voltage direct current controller's impact on optimization of generation cost, voltage deviation, and total power system losses.

关键词： Optimal power flow particle swarm optimization algorithm power system optimization unified optimal power flow modeling voltage source converter based high voltage direct current

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Performance analysis of hybrid geometric-probabilistic shaped 8/12 quadrature amplitude modulation for coherent optical communication system

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OPTICAL ENGINEERING 2023年第6期62卷 068104-068104页

作者： Xu, Junping Ma, Jie Liu, Jianfei Lu, Jia Zeng, Xiangye Luo, Mingming Hebei Univ Technol Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin Peoples R China Hebei Key Lab Adv Laser Technol & Equipment Tianjin Peoples R China

. A hybrid shaping (HS) scheme based on geometric shaping (GS) and probabilistic shaping (PS) in a coherent optical communication system is proposed. A particle swarm optimization algorithm and Maxwell-Boltzmann distribution are employed to sequentially implement GS and PS. The results demonstrate that hybrid shaped 8/12-ary quadrature amplitude modulation (HS-8/12QAM) is superior to regular-8/12QAM (R-8/12QAM) in terms of reducing the bit error rate (BER) and increasing the generalized mutual information (GMI). HS-8QAM achieves a 2 dB optical signal-to-noise ratio (OSNR) gain and 0.45bits/symbol GMI gain compared with R-8QAM. Meanwhile, HS-12QAM achieves 1.9 dB OSNR gain and 0.68 bits/symbol GMI gain compared with R-12QAM. In addition, HS-8/12QAM is better than R-8/12QAM in terms of transmission distance and data rate.

关键词： coherent optical communication particle swarm optimization algorithm hybrid shaping generalized mutual information

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Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

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PHYSICAL COMMUNICATION 2023年第1期57卷

作者： Tan, Fangqing Xu, Xu Chen, Hongbin Li, Shichao Guilin Univ Elect Technol Guangxi Key Lab Wireless Wideband Commun & Signal Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Telecommun Guilin 541004 Peoples R China

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input-single-output system. In particular, considering the system circuits' energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user' rate requirement, the BS's maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element's working state is controlled by a circuit switch, each reflection element's switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Reconfigurable intelligent surface Energy efficiency Beamforming Sine cosine algorithm particle swarm optimization algorithm

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Optimal design and dynamic performance analysis of a fractional-order electrical network-based vehicle mechatronic ISD suspension

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 184卷

作者： Shen, Yujie Hua, Jie Fan, Wei Liu, Yanling Yang, Xiaofeng Chen, Long Jiangsu Univ Res Inst Automot Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

To further explore the beneficial effects of the vibration isolation performance of a new vehicle ISD (inerter-spring-damper) suspension using a mechatronic inerter, this paper proposes a novel optimal design methodology for a vehicle mechatronic ISD suspension system based on a fractional-order electrical network is proposed. In view of difficulties in the engineering imple-mentation of fractional-order mechanical network elements, this paper first studies the corre-sponding relationship and analytical expression of fractional-order mechanical and electrical network elements respectively. Under the instruction of fractional calculus, the fractional-order electrical network elements are used to realize equivalent fractional-order mechanical network elements by adopting a ball-screw mechatronic inerter. Then, a quarter car dynamic model of vehicle mechatronic ISD suspension is constructed, and the parameters of the fractional-order electrical network and the integer-order electrical network are obtained by means of particle swarm optimization algorithm. The performance advantages of vehicle mechatronic ISD sus-pension using a fractional-order electrical network are verified by numerical simulations and experiments. The results show that, compared with the application of an integer-order electrical network, the vibration suppression performance of a vehicle mechatronic ISD suspension can be further enhanced by using a fractional-order electrical network.

关键词： Vehicle suspension Mechatronic inerter Fractional-order electrical network particle swarm optimization algorithm Optimal design

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A simulation-based optimization for deploying multiple kinds road rescue vehicles in urban road networks

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期181卷

作者： Tan, Zheyi Zhang, Qian Deng, Weiliang Zhen, Lu Shao, Wei Shanghai Univ Sch Management Shang Da Rd 99 Shanghai 200444 Peoples R China Shanghai Univ Sci Res Management Dept Shang Da Rd 99 Shanghai 200444 Peoples R China

In the rescue process of urban road traffic accidents, the decision-making support system is of great importance since it will affect the rescue response time, and the response time is used as an evaluation index of the system performance. A shorter response time can ensure that casualties are rescued timely and can help restore the road as soon as possible. This paper provides a decision tool for traffic accident rescue vehicles' integrated deployment problem in urban areas. In order to better simulate the stochastic rescue requests, the time and space distribution of traffic, and the dynamic rescue process, a simulation optimization model is established. This model determines the optimal deployment plan for three kinds of road rescue vehicles, i.e., ambulances, wreckers, and sweepers, in several time intervals in one day. To find a better solution, we incorporate a particle swarm optimization (PSO) method into the simulation and optimization procedures. The proposed method is validated by a case study in Shanghai. Additionally, sensitivity analysis is included to study the effects of various parameters and to confirm effectiveness and efficiency of the proposed method. The numerical experiments indicate that simply increasing the number of road rescue resources does not always improve the efficiency of road rescue.

关键词： Traffic accident rescue Vehicle deployment Simulation optimization particle swarm optimization algorithm

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Optimizing the Operation Sequence of Chip Shooter Machine Based on the particle swarm optimization algorithm

Optimizing the Operation Sequence of Chip Shooter Machine Ba...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Liang, Wanlei Guan, Xiaodan North China Inst Aerosp Engn Dept Elect Engn Langfang 065000 Peoples R China

ISBN: (纸本)9783037855034

The mounting process is the key factor of the placement efficiency, it is also important for the improvement of the efficiency of whole production line and decrease of the cost. This paper analyzed the mounting process of the Chip Shooter machine, applied the PSO algorithm, constructed the corresponding coding system, proposed the corresponding particle update mechanism, introduced the partially matched crossover idea of the genetic algorithm into the PSO algorithm, and designed the new re-scheduling method of feeder position assignment to optimize the position assignment of feeders and the pickup and placement sequence of components, thus improved the placement efficiency. After comparing the results before and after the simulation test for selected 8 pieces of PCB, the average efficiency of this algorithm is 7.09% higher than genetic algorithm method that is based on sort encoding. The experimental result shows that, this algorithm is more efficiency on the improvement placement efficiency and decrease of the placement time for the chip shooter machine.

关键词： Chip shooter machine Operation sequence optimization particle swarm optimization algorithm

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Constrained Multi-objective particle swarm optimization algorithm

Constrained Multi-objective Particle Swarm Optimization Algo...

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8th International Conference on Intelligent Computing (ICIC)

作者： Gao, Yue-lin Qu, Min N Ethn Univ Inst Informat & Syst Sci Yinchuan Ningxia Peoples R China

ISBN: (纸本)9783642318368

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

关键词： particle swarm optimization algorithm constrained multi-objective optimization Pareto neighborhood crossover operation penalty function method

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