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Flood Routing: Improving Outflow Using a New Non-linear Muskingum Model with Four Variable Parameters Coupled with pso-ga algorithm

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WATER RESOURCES MANAGEMENT 2020年第10期34卷 3291-3316页

作者： Akbari, Reyhaneh Hessami-Kermani, Masoud-Reza Shojaee, Saeed Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

Flood is one of the most destructive natural disasters that damages people's lives dramatically. Thus, it is crucial for researchers and politicians to research flood routing. The non-linear Muskingum model has been significantly considered by engineers and researchers in flood routing. In this study, in order to increase the accuracy of outflow prediction, the new non-linear Muskingum model, with four variable parameters, is proposed for the first time. In the proposed model, the inflows are divided into three sub-regions, and each of the four hydrologic parameters has a various value in each sub-region. How to select the sub-regions, as well as the values of the hydrologic parameters, is determined by combining both the Particle Swarm Optimization and Genetic algorithm. The proposed model is studied in four case studies. Compared to the non-linear Muskingum model with three parameters, the amount of sum squared deviation (SSQ) decreased 52 and 6.9% for the first and second case studies, respectively. Compared to the best variable parameter model, the SSQ for the third and fourth case studies reduced 76 and 62%, respectively. The results showed that the SSQ was considerably decreased significantly in all of the four case studies, and the proposed model has superiority over other non-linear Muskingum models, which have been used by other researchers so far.

关键词： Four parameter Muskingum model Karoon River pso-ga algorithm Natural disasters

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Multi-task equilibrium scheduling of Internet of Things: A rough set genetic algorithm

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COMPUTER COMMUNICATIONS 2022年 184卷 42-55页

作者： Bu, Bing North China Univ Technol Sch Civil Engn Beijing 100144 Peoples R China

The Internet of Things (IoT) server suffers from numerous business traffic with network bandwidth growth, resulting in downtime. Providing business support without affecting the user experience is the primary problem that IoT companies need to consider and solve in the face of traffic impact. This paper proposes a load balancing scheduling algorithm based on Particle Swarm Optimization Genetic algorithm (pso-ga) for IoT clusters. The algorithm uses CPU occupancy rate, memory occupancy rate, network bandwidth occupancy rate, and disk Input and Output (IO) occupancy rate to comprehensively measure the server node load and establish a resource balance model. The fitness function is used to quantify the influence as the basis of weight adjustment. Then, the Particle Swarm Optimization (pso) algorithm uses the disturbance factor and contraction operator. The optimized algorithm is used to calculate the optimal solution of the fitness function and obtain the optimal weight. Finally, the pso-ga algorithm is simulated, tested, and compared with the other three load balancing algorithms. As seen from the test results of response delay, throughput, request error rate, and resource utilization, the performance of this algorithm is improved by more than 5% compared with the performance of the traditional method, and the optimization ability is improved obviously. The research content of this paper provides a new way to alleviate the network load, reduce the server overload, congestion, downtime, and other problems, and realize the multi-task balanced scheduling of IoT.

关键词： Internet of Things Network load Task balancing scheduling pso-ga algorithm Fitness function design

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Intelligent Fuzzy Controller Design: Disturbance Rejection Cases

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APPLIED SOFT COMPUTING 2022年 124卷

作者： Moradi, M. Seyedtabaii, S. Shahed Univ Elect Engn Dept Tehran Iran

In this paper, a modified fuzzy gain scheduling (GS) Proportional-Integral-Derivative (PID) controller where the output of the integral term of PID, ui, heuristically used as the scheduling variable is discussed. Then, it is demonstrated that ui can effectively represent the inaccessible low-frequency disturbances that alter the system operating point. In addition, since GS design involves timeconsuming multiple runs of nonlinear optimization tasks, a fast hybrid method using pso-ga and classical fminsearch is worked out. Interpolation of the designs, for GS build up, is carried out using an adaptive neuro-fuzzy inference system (ANFIS) to provide PID continuous gain surfaces. The algorithm is applied to the speed control of a combined cycle power plant under unmeasured bounded load disturbances. The simulation outputs indicate that the design quality is effectively preserved in case of small perturbations where the achievements are also superior to the responses of some published methods. In the case of large perturbations, the fuzzy scheduling PID (FSPID) supersedes successfully two other optimally designed regulators. Therefore, it is concluded that the proposed approach renders a better transient response in facing both small and large stepwise load disturbances. The findings have been verified under various working loads through extensive simulations. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Nonlinear system Combined cycle power plant pso-ga algorithm Intelligent control Fuzzy control

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Integrated Timetable Optimization for Minimum Total Energy Consumption of an AC Railway System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第4期69卷 3641-3653页

作者： Pan, Ziqiang Chen, Minwu Lu, Shaofeng Tian, Zhongbei Liu, Yuanli Natl Rail Transportat Elect & Automat Engn Techno Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 510640 Guangdong Peoples R China Univ Birmingham Sch Elect Elect & Syst Engn Birmingham B15 2TT W Midlands England

The optimization of train trajectory and timetable offers two effective methods of energy-efficient operations. Most traditional research optimizes them separately and thus the global optimality cannot be achieved. Previous work has mainly concentrated on minimizing the mechanical energy consumption of trains, not the electrical energy. This paper aims to develop an integrated model to optimize the integrated timetable, which includes both the timetable and the train trajectory achieve the minimum electrical energy consumption. The AC power supply system model is first proposed to calculate the parameters of the AC power supply network. Then, a model is developed to calculate various energy consumptions using power flow calculation. Furthermore, an integrated timetable optimization model for minimum total energy consumption of AC railway system is proposed and the optimal integrated timetable is obtained by the proposed hybrid ga-pso algorithm. Finally, 5 real-world case studies based on the Lanzhou-Xinjiang high-speed railway are presented. The results show that the proposed integrated model can achieve a reduction in total energy consumption for the entire line up to 14.3% compared with the previous optimization model. The hybrid ga-pso algorithm achieves the best results compared with the results achieved by the ga and pso algorithms applied along.

关键词： AC railway system high-speed railway integrated model train trajectory pso-ga algorithm AC power flow calcultion timetable

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Green and sustainable tunnel maintenance activities scheduling under uncertainty

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JOURNAL OF CLEANER PRODUCTION 2021年 297卷 126689-126689页

作者： Sun, Yufeng Hu, Min Lin, Shumin Shanghai Univ SILC Business Sch 20th Chengzhong Rd Shanghai 201800 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China Shanghai Univ SHU SUCG Res Ctr Bldg Industrializat Shanghai 200072 Peoples R China

The maintenance of tunnel infrastructure is fundamental to the reliability, safety, and efficiency of tunnel operations. However, environmental deterioration has dramatically changed maintenance scheduling of tunnel infrastructure. Therefore, the trade-off between maintenance cost and environmental impact is crucial when formulating maintenance schedules. This paper extends the preventive maintenance scheduling problem (PMSP) from three perspectives: social impact, environmental impact, and unexpected maintenance over the entire planning horizon. We then propose a stochastic bi-objective integer programming model to minimize the total cost and CO2 emissions of tunnel maintenance over the entire planning horizon. The model is applied to a case study developed for the Dalian Road Tunnel in Shanghai. A scenario-based method is adopted to account for uncertain failures. A hybrid algorithm using particle swarm optimization (pso) and genetic algorithm (ga) is proposed to solve the model in realistic largescale environments. Extensive numerical experiments are performed to verify the effectiveness of the proposed model and the efficiency of the proposed algorithm. Some meaningful management implications are revealed based on the experimental results. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Maintenance Social and environmental impacts Tunnel infrastructure Stochastic programming pso-ga algorithm

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Numerical optimization and analysis on vibration characteristics of bicycles based on the novel CA-pso algorithm

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JOURNAL OF VIBROENGINEERING 2017年第6期19卷 4018-4032页

作者： Zhang, Jin gao, Jiang Hua Wu, Jian Guo Changchun Finance Coll Dept Phys Educ Changchun 130022 Jilin Peoples R China Rizhao Polytech Student Affairs Off Rizhao 276826 Peoples R China Hunan Univ Finance & Econ Sch Math & Stat Changsha 410205 Hunan Peoples R China

Regarding the published researches on bicycles, they fail to design fatigue characteristics of the bicycle. In addition, dynamics and fatigue characteristics are not further improved by using advanced optimization algorithms. Aiming at these questions, this paper tries to optimize dynamics and fatigue characteristics of the bicycle through combining finite element model with advanced algorithms. The advanced algorithm applies ideas of cellular automation (CA) to Particle Swarm Optimization (pso), and then a hybrid CA-pso algorithm is proposed. Moreover, the finite element model is also validated by experimental test. Computational results show that: the maximum stress of bicycles is mainly distributed on the frame, especially on joints of different round pipes at different moments mainly because a dead corner is at the joint, and the dead corner can easily cause stress concentration. Under alternating forces, the stress concentration at joints will cause fatigue damage. Therefore, the service life of this position will be the shortest. As a result, the dynamics and fatigue characteristics of the joint position are taken as the optimized objective. In order to verify the optimized effectiveness of the proposed CA-pso algorithm in the paper, the widely used pso algorithm and pso-ga algorithm are also used to optimize the bicycle. When the traditional pso algorithm is used to optimize the bicycle, the root-mean-square value and maximum difference of vibration accelerations are decreased by 11.9 % and 14.3 %. When the pso-ga algorithm is used to optimize the bicycle, the root-mean-square value and maximum difference of vibration accelerations are decreased by 20.3 % and 12.9 %. When the proposed CA-pso algorithm is used to optimize the bicycle, the root-mean-square value and maximum difference of vibration accelerations are decreased by 27.1 % and 18.6 %. Compared with other two kinds of pso algorithms, optimized effects of vibration accelerations are very obvious. In a

关键词： dynamics fatigue life bicycles vibration accelerations root-mean-square value maximum difference pso algorithm pso-ga algorithm CA-pso algorithm

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Credit Risk Assessment Model for Cross-border E-commerce in a BP Neural Network Based on pso-ga

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 411-414页

作者： Zhang, Xinmin Zhao, Xumin Wu, Na Xingtai Univ Xingtai 054001 Hebei Peoples R China

With the rapid development of cross-border e-commerce in China, a variety of factors that threaten information related to this subject are increasing on a large scale, thereby affecting the stable operation of this type of e-commerce. A credit risk assessment model for cross-border e-commerce in a backpropagation (BP) neural network based on particle swarm optimization-genetic algorithm (pso-ga) was analyzed in this study. pso-ga was introduced, and the BP neural network and its structure were explained. Finally, the construction process of the credit risk assessment model for cross-border e-commerce in a BP neural network based on pso-ga was presented. Results show that the aforementioned model can effectively fulfill the requirements for credit risk assessment in cross-border e-commerce.

关键词： pso-ga algorithm BP neural network electronic commerce credit risk assessment

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