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Real-time power optimization strategy for fuel cell ships based on improved genetic simulated annealing algorithm

ELECTRIC POWER SYSTEMS RESEARCH 2025年 245卷

作者： Zhang, Qinjin Song, Liming Zeng, Yji Liu, Yancheng Liu, Siyuan Wang, Ning Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

In alignment with the global pursuit of energy conservation and emission reduction, the utilization of hydrogen fuel cells (FCs) shipboard vessels are increasingly recognized as a potent means to mitigate environmental impact. However, despite its promising potential, the elevated costs associated with FCs and hydrogen fuel currently pose a significant challenge to market competitiveness. To maximize the economic viability of FCBattery hybrid system in maritime applications, this study introduces an innovative real-time energy management strategy (EMS) that refines the genetic simulated annealing algorithm (G-SA) to mitigate FC degradation and enhance overall system cost-effectiveness by integrating the concept of Equivalent Hydrogen Consumption (EHC). The crux of this strategy lies in the implementation of a Nonlinear Autoregressive Neural Network model (NAR-NET), which facilitates the real-time forecasting of vessel load demands. By embedding the EHC of the hybrid power system and the degradation factor of FCs into the objective function, the strategy employs G-SA to determine the optimal power allocation in real-time. Compared with the Equivalent Consumption Minimization Strategy (ECMS), the proposed strategy is shown to achieve a 13 % to 30 % reduction in equivalent fuel consumption and approximately a 34 % slowdown in the rate of performance degradation.

关键词： Fuel cell vessels Degradation Nonlinear autoregressive neural network Energy management strategy Improved genetic simulated annealing algorithm

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Bi-objective optimization framework for prefabricated construction service combination selection using genetic simulated annealing algorithm

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第10期31卷 3921-3945页

作者： Du, Qiang Qi, Xiaomin Zou, Patrick X. W. Zhang, Yanmin Changan Univ Ctr Green Engn & Sustainable Dev Xian Peoples R China Changan Univ Sch Econ & Management Xian Peoples R China Changan Univ Engn Res Ctr Rd Infrastructure Digitizat Minist Educ Xian Peoples R China

PurposeThe purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the ***/methodology/approachThe weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the *** results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low ***/valueThe contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

关键词： Prefabricated construction service Synergistic effect Quality of service genetic simulated annealing algorithm

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Optimization of node voltage stability of flexible DC transmission network based on genetic simulated annealing algorithm 24

Optimization of node voltage stability of flexible DC transm...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Lin, Xiaolang Chen, Zexi Liu, Jinsheng Li, Yan Shenzhen Power Supply Co Ltd Shenzhen 518000 Guangdong Peoples R China

ISBN: (纸本)9798400716638

In order to ensure the safety of power system and improve the working efficiency of distribution network, the voltage stability optimization of flexible DC transmission network nodes based on genetic simulated annealing algorithm is proposed. The original operation data is equivalent to the product of the above three matrices, and the parameters are sorted in descending order to obtain the preprocessing result of node voltage data of flexible DC transmission network;The system characteristics related to voltage stability are extracted, and the voltage stability optimization results of flexible DC transmission network nodes are obtained, that is, the voltage stability optimization is realized. The experimental results show that the proposed method has better optimization effect, higher coincidence between the optimized waveform and the actual waveform, and better voltage stability optimization performance under the condition of no interference and interference.

关键词： genetic simulated annealing algorithm Flexible DC transmission network Node voltage Stability

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Coherent Doppler lidar wind retrieval for a typhoon based on the genetic simulated annealing algorithm

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Chinese Optics Letters 2024年第4期22卷 1-7页

作者：刘珍原禄城汤杰张云鹏刘继桥竹孝鹏杨巨鑫时伟陈卫标 Aerospace Laser Technology and Systems Department Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBejing 100049China Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030China

A method of spectrum estimation based on the genetic simulated annealing(GSA)algorithm is proposed,which is applied to retrieve the three-dimensional wind field of typhoon Nangka observed by our research *** to the genetic algorithm(GA),the GSA algorithm not only extends the detection range and guarantees the accuracy of retrieval results but also demonstrates a faster retrieval *** results indicate that both the GA and GSA algorithms can enhance the detection range by 35%more than the least squares ***,the convergence speed of the GSA algorithm is 17 times faster than that of the GA,which is more beneficial for real-time data processing.

关键词： coherent Doppler lidar three-dimensional wind field retrieval genetic simulated annealing algorithm spectrum estimation typhoon

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Optimized Design of Heliostat Field Based on genetic simulated annealing algorithm

Optimized Design of Heliostat Field Based on Genetic Simulat...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Tan, Zhaowen Xi'An Kedagaoxin University Xi'an China

ISBN: (纸本)9798350309614

Multi-setting heliostat is the basic formation of tower solar thermal power station to collect solar energy. It is an important measure to build a new type of power system which is mainly based on new energy Title. In this paper, the efficiency and thermal power output of different sizes and mounting heights of heliostats are evaluated by simulation or numerical computation, and the optimal combination is found, and the number and location of heliostats are determined according to the optimal combination. First, given the location of the centers of all heliostats, the annual average optical efficiency, the annual average output thermal power, and the annual average output thermal power per unit mirror area are calculated for that heliostat field. Then, considering the case where all the heliostats have the same size and mounting height, Monte Carlo numerical simulation is used to design the parameters of the heliostat field so that the annual average output thermal power per unit mirror area is as large as possible when the heliostat field reaches the rated power. Finally, considering the situation that the size and mounting height of the heliostat mirror can be different, the genetic simulated annealing algorithm is used to optimize and redesign the parameters of the heliostat mirror field, so that the annual average output thermal power per mirror area is as large as possible under the condition of reaching the rated power. The proposed genetic simulated annealing algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, and has high flexibility and applicability. © 2023 IEEE.

关键词： genetic simulated annealing algorithm output thermal power solar energy utilization unit mirror area

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A Novel genetic simulated annealing algorithm for No-wait Hybrid Flowshop Problem with Unrelated Parallel Machines

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ISIJ INTERNATIONAL 2021年第1期61卷 258-268页

作者： Xuan, Hua Zheng, Qianqian Li, Bing Wang, Xueyuan Northeaster Univ Shenyang Liaoning Peoples R China Zhengzhou Univ Sch Management Engn 100 Sci Rd Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ 100 Sci Rd Zhengzhou 450001 Henan Peoples R China Southweast Jiaotong Univ Chengdu Sichuan Peoples R China

This paper studies the problem of scheduling N jobs in a hybrid flowshop with unrelated parallel machines at each stage. Considering the practical application of the presented problem, no-wait constraints and the objective function of total flowtime are included in the scheduling problem. A mathematical model is constructed and a novel genetic simulated annealing algorithm so-called GSAA are developed to solve this problem. In the algorithm, firstly a modified NEH algorithm is proposed to obtain the initial population. A two-dimensional matrix encoding scheme for scheduling solutions is designed and an insertion-translation approach are employed for decoding in order to meet no-wait constraints. Afterwards, to avoid GA premature and enhance search ability, an adaptive adjustment strategy is imposed on the crossover and mutation operators. In addition, a SA procedure is implemented for some better individuals from the GA solutions to complete re-optimization, where five neighborhood search structures are constructed including job based exchange, gene based exchange, gene based insertion, job based mutation, and gene based mutation. Finally, various simulation experiments in two scales of small-medium and large are established. Computational results show that the presented algorithm performs much more effectively compared with several heuristic algorithms reported in the literature.

关键词： hybrid flowshop unrelated parallel machines no-wait constraints total flowtime genetic simulated annealing algorithm

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Research on Logistics Distribution Routing Problem of Unmanned Vehicles Based on genetic simulated annealing algorithm 2021

Research on Logistics Distribution Routing Problem of Unmann...

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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Hou, Xiaohui Ji, Yuan Liu, Weilong Han, Jinheng Zhang, Junzhi He, Chengkun Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450390200

Nowadays, unmanned logistics distribution has become a necessity for development. This paper proposes a vehicle routing model for logistics scheduling and distribution intending to minimize the distribution cost by integrating intelligent scheduling and path planning systems. The genetic simulated annealing algorithm is designed combining with the genetic algorithm and the simulated annealing algorithm. In this scheme, electric unmanned vehicles are used for urban logistics distribution to improve efficiency and reduce costs. At the same time, factors such as vehicle load, customer time window, and vehicle mileage limit are taken into consideration. Finally, the effectiveness and superiority of the proposed algorithm are verified by applying it to solve a specific case.

关键词： Unmanned transport vehicle vehicle routing problem genetic simulated annealing algorithm logistics scheduling and distribution

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Slope Stability Analysis Using genetic simulated annealing algorithm in Conjunction with Finite Element Method

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KSCE JOURNAL OF CIVIL ENGINEERING 2020年第1期24卷 30-37页

作者： Cen, Weijun Luo, Jiarui Yu, Jishun Shamin Rahman, M. Hohai Univ Coll Water Conservancy Hydropower Engn Nanjing Peoples R China N Carolina State Univ DeptCivilConstructionEnvironm Engn Raleigh NC 27695 USA

The calculation of factor of safety and the determination of the critical slip surface (a slip surface with the minimal factor of safety) are essential steps in stability analysis of soil slopes. In this study, a genetic simulated annealing algorithm (GSA), which combines the genetic algorithm and the simulated annealing algorithm, is proposed to quickly and accurately locate the critical slip surface of soil slopes. The corresponding factors of safety of slip surfaces are calculated based on the stress field of soil slopes that are computed by finite element method (FEM). The search ability of GSA for the critical slip surface of an earth-rock dam slope is proved to be more accurate and efficient compared with the golden-section method based on the same stress field calculated by FEM. In addition, a practical slope with complex soil layering is presented to further illustrate the reasonability of the calculation results of GSA in conjunction with FEM. The numerical simulations shows that the factor of safety and the critical slip surface determined using GSA with FEM is in good agreement with that using the shear strength reduction method.

关键词： Slope stability genetic simulated annealing algorithm Finite element method Factor of safety Slip surface

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Service Deployment of C4ISR Based on genetic simulated annealing algorithm

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IEEE ACCESS 2020年 8卷 65498-65512页

作者： Jiao, Zhiqiang Zhang, Jieyong Yao, Peiyang Wan, Lujun Ni, Lei Air Force Engn Univ Grad Coll Xian 710077 Peoples R China Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China China Elect Technol Grp Corp Res Inst 28 Key Lab Informat Syst Engn Nanjing 210007 Peoples R China Air Force Engn Univ ACT Nav Xian 710077 Peoples R China

This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

关键词： Service deployment C4ISR deployment constraints multi-objective optimization genetic simulated annealing algorithm

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Research on the Calibration of Binocular Camera Based on BP Neural Network Optimized by Improved genetic simulated annealing algorithm

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IEEE ACCESS 2020年 8卷 103815-103832页

作者： Chen, Long Zhang, Fengfeng Sun, Lining Soochow Univ Sch Mech & Elect Engn Suzhou 215006 Peoples R China Soochow Univ Collaborat Innovat Ctr Suzhou Nano Sci & Technol Suzhou 215123 Peoples R China

The Back Propagation (BP) neural network has the problems of low accuracy and poor convergence in the process of binocular camera calibration. A method based on BP neural network optimized by improved genetic simulated annealing algorithm (IGSAA-BP) is proposed to solve these problems to complete the binocular camera calibration. The method of combining Gaussian scale space and Harris corner detection operator is used for corner detection. A matched algorithm of homonymous corner is proposed by combining point-to-point spatial mapping and grid motion statistics. The pixel values of the homonymous corner and three-dimensional coordinate values are taken as the input and output of BP neural network respectively. The crossover and mutation probability of genetic simulated annealing algorithm and the annealing criterion are improved, the IGSAA-BP neural network is used to calibrate the binocular camera. The average calibration accuracy of BP neural network and IGSAA-BP neural network is 0.71mm and 0.03mm, respectively. The average calibration accuracy of binocular camera is improved by 96%. The iteration speed is increased by 20 times and global optimization ability is improved. It can be seen that the IGSAA-BP neural network can improve the calibration accuracy of binocular camera and accelerate convergence speed.

关键词： Camera calibration genetic simulated annealing algorithm Gaussian scale space corner detection homonymous corner match BP neural network

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