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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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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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Configuration optimization method of Hadoop system performance based on genetic simulated annealing algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8965-S8973页

作者： Luo, Xiaoling Fu, Xueliang Inner Mongolia Agr Univ Hohhot 010018 Peoples R China

The configuration optimization method of Hadoop system performance based on genetic simulated annealing algorithm is studied. In view of the performance of Hadoop on open source cloud computing platform, an optimization method is proposed. Based on the genetic simulated annealing algorithm, each configuration scheme is used as a chromosome for multiple selection, crossover and mutation. Combined with the principle of simulated annealing, the survival of the new chromosome and the number of iterations of the whole algorithm are controlled, and the optimal scheme of the system configuration is found. The experimental results show that the method can effectively improve the operation efficiency of the operation. In addition, the overall effect of the group is very good at the end of the iteration. When the job types in the system are similar, according to the characteristics that the whole simulated annealing algorithm is approaching the optimal solution, a real-time optimization configuration model is proposed on the basic of genetic simulated annealing algorithm.

关键词： genetic simulated annealing algorithm Hadoop System performance Configuration optimization

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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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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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genetic simulated annealing algorithm for Task Scheduling based on Cloud Computing Environment

Genetic Simulated Annealing Algorithm for Task Scheduling ba...

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2010 International Conference on Intelligent Computing and Integrated Systems

作者： GAN Guo-ning,HUANG Ting-lei,GAO Shuai School of Computer science and engineering Guilin University of Electronic Technology Guilin,China

Scheduling is a very important part of the cloud computing *** paper introduces an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its *** considers the QOS requirements of different type tasks,the QOS parameters are dealt with *** algorithm efficiently completes tasks scheduling in the cloud computing environment computing.

关键词： cloud computing task scheduling genetic simulated annealing algorithm QOS

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genetic simulated annealing algorithm for QoS Routing

Genetic Simulated Annealing Algorithm for QoS Routing

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2012年计算机应用与系统建模国际会议

作者： Zhang Hui Huang Jihai Information Engineering College Zhongzhou University

This paper proposes the math model of QoS routing. Then we present a genetic simulated annealing algorithm is proposed of QoS routing. The results show that the new method to enhance the search ratio and the speed of ... 详细信息

关键词： QoS routing delay constraint search ratio genetic simulated annealing algorithm

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Water Distribution System Optimization Using genetic simulated annealing algorithm

Water Distribution System Optimization Using Genetic Simulat...

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International Conference on Intelligent Computing and Information Science

作者： Shu, Shihu Tongji Univ Sch Environm Sci & Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9783642181337

Water supply system optimization makes use of the latest advances in hybrid genetic algorithm to automatically determine the least-cost pump operation for each pump station in large-scale water distribution system while satisfying simplified hydraulic performance requirements. Calibration results of the original model were pretty good. The comparison results show that the difference between the simplified and the original mode simulation of nodal pressure and pipe flow was less than 5%. The precision of the simplified model could satisfy the requirement for pump operation optimization. When the simplified model was used instead of the microscopic model, the calculation time of optimal control model was greatly saved, but the precision was basically the same. The results of optimal control model for the water distribution system in City S showed that the simplified model for optimal control had remarkable advantage cause the calculation time of optimal control model was greatly saved, but the precision was basically same.

关键词： Water distribution system pump operation optimization simplified model genetic simulated annealing algorithm

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