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The optimization study of core power control based on meta-heuristic algorithm for China initiative accelerator driven subcritical system

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第2期55卷 452-459页

作者： Li, Jin-Yang Du, Jun-Liang Gu, Long Zhang, You-Peng Lin, Cong Wang, Yong-Quan Zhou, Xing-Chen Lin, Huan Lanzhou Univ Sch Nucl Sci & Technol Lanzhou 730000 Peoples R China Lanzhou Univ Southeast Res Inst Putian 351106 Peoples R China Chinese Acad Sci Inst Modern Phys Lanzhou 730000 Peoples R China Fudan Univ Inst Modern Phys Shanghai 200433 Peoples R China

The core power control is an important issue for the study of dynamic characteristics in China initiative accelerator driven subcritical system (CiADS), which has direct impact on the control strategy and safety analysis process. The CiADS is an experimental facility that is only controlled by the proton beam intensity without considering the control rods in the current engineering design stage. In order to get the optimized operation scheme with the stable and reliable features, the variation of beam intensity using the continuous and periodic control approaches has been adopted, and the change of collimator and the adjusting of duty ratio have been proposed in the power control process. Considering the neutronics and the thermal-hydraulics characteristics in CiADS, the physical model for the core power control has been established by means of the point reactor kinetics method and the lumped parameter method. Moreover, the multi-inputs single-output (MISO) logical structure for the power control process has been constructed using proportional integral derivative (PID) controller, and the meta-heuristic algorithm has been employed to obtain the global optimized parameters for the stable running mode without producing large perturbations. Finally, the verification and validation of the control method have been tested based on the reference scenarios in considering the disturbances of spallation neutron source and inlet temperature respectively, where all the numerical results reveal that the optimization method has satisfactory performance in the CiADS core power control scenarios.(c) 2022 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Control strategy Optimization Accelerator driven system Proportional integral derivative meta-heuristic algorithm

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Location-allocation problem for intra-transportation system in a big company by using meta-heuristic algorithm

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SOCIO-ECONOMIC PLANNING SCIENCES 2013年第4期47卷 309-317页

作者： Hamadani, Ali Zeinal Ardakan, Mostafa Abouei Rezvan, Taghi Honarmandian, Mohammad Mehran Isfahan Univ Technol Dept Ind Engn Esfahan *** Iran Mobarakeh Steel Co Esfahan *** Iran

This paper presents the location-allocation problem of parking facilities in Mobarakeh Steel Company. The aim is to find the optimal location for the parking facilities and allocate travels between departments to each parking facility. To optimize the cost and facing the transportation demands of all departments, a mathematical model is designed and solved by a commercial software package. Also, the number of vehicles allocated to each parking facility is determined. A hybrid meta-heuristic algorithm combining a genetic algorithm with parallel simulated annealing is developed which can be used even in more complicated structure of the problem. The results show that this hybrid algorithm is highly efficient in solving this kind of problems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Transportation Location-allocation problem meta-heuristic algorithm

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A comparative study on predicting the rapid chloride permeability of self-compacting concrete using meta-heuristic algorithm and artificial intelligence techniques

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STRUCTURAL CONCRETE 2022年第2期23卷 753-774页

作者： Yuan, Jia Zhao, Ming Esmaeili-Falak, Mahzad Jiamusi Univ Sch Architectural Engn Jiamusi 154007 Heilongjiang Peoples R China Jiamusi Univ Sch Fine Arts Jiamusi Heilongjiang Peoples R China Islamic Azad Univ Dept Civil Engn North Tehran Branch Tehran Iran

Obtaining a trustworthy approach to forecast the chloride penetration into self-compacting concrete via rapid test may lead to frugality in cost, time, and energy to provide a durable mix design. Different single and hybrid regression methods are developed to predict the results of rapid chloride penetration tests in the present study. Cement content, fly ash, and silica fume replacement percent with cement, temperature and fine and coarse aggregates are considered as input variables. All predicted values using expanded models have a good agreement with experimentally measured results. Evaluating the accuracy and precision of single and hybrid optimized models by five statistical performance criteria (R2, root mean square error, mean absolute error, mean absolute percentage error, and performance index) illustrates that the hybrid support vector regression with optimization algorithm is a high-accurate promising model for predicting the results of a rapid chloride penetration test.

关键词： fly ash meta-heuristic algorithm prediction rapid chloride permeability regression methods silica fume

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APPLICATION OF HYBRID meta-heuristic algorithm FOR ASSIGNING REAL-TIME TASKS TO HETEROGENEOUS PROCESSORS 6

APPLICATION OF HYBRID META-HEURISTIC ALGORITHM FOR ASSIGNING...

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6th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Marimuthu, Poongothai Arumugam, Rajeswari Coimbatore Inst Technol Dept Elect & Commun Engn Coimbatore 641014 Tamil Nadu India

ISBN: (纸本)9781479979837

Heterogeneous multiprocessor system has the capability for providing low cost and high performance. Nevertheless, in order to take advantage of computing power of heterogeneous system, it is essential to use an efficient scheduling algorithm for task allocation to the available processors. This research mainly focuses on the development of task assignment algorithm for heterogeneous multiprocessor system. Assigning real-time tasks in heterogeneous multiprocessor are in general a challenging problem and NP hard. In this paper, Hybrid Max-Min Ant colony optimization algorithm (H-MMAS) is proposed to solve the real-time task assignment problem in heterogeneous multiprocessor. The objective of the proposed algorithm is to get a feasible task assignment solution, and to optimize energy consumption of every feasible task assignment solution. The performance of the proposed H-MMAS algorithm has been tested for consistent and inconsistent heterogeneous multiprocessor systems. Experimental comparisons with existing Modified BPSO algorithm demonstrate the effectiveness of the proposed H-MMAS algorithm.

关键词： ACO Real-time task assignment Heterogeneous processors meta-heuristic algorithm Local search algorithms

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Fault Location on Three-Terminal Transmission Lines Based on a meta-heuristic algorithm 47

Fault Location on Three-Terminal Transmission Lines Based on...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Huynh, Duy C. Truong, Thanh H. Dunnigan, Matthew W. Ho Chi Minh City Univ Technol HUTECH Ho Chi Minh City Vietnam Heriot Watt Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9781665435543

This paper proposes a fault location technique on three-terminal transmission lines based on a solution to the optimization problem. The cuckoo search (CS) algorithm is improved and applied to solve this fault location. The CS algorithm modifies the constant step size into the chaotic step size to achieve the globally optimal solution through the chaotic behavior, called the Chaos CS (CCS) algorithm. The obtained numerical results of applying the CCS algorithm are compared to those of applying the CS algorithm and genetic algorithm (GA). The comparison shows the effectiveness of the proposal of the fault location on the three-terminal transmission lines.

关键词： fault location three-terminal transmission lines meta-heuristic algorithm

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A Hyperheuristic and Reinforcement Learning Guided meta-heuristic algorithm Recommendation 27

A Hyperheuristic and Reinforcement Learning Guided Meta-heur...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Zhu, Ningning Zhao, Fuqing Cao, Jie Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Automatic selection of the most appropriate algorithms for complex optimization problems has emerged as a cutting-edge trend in artificial intelligence. This approach circumvents the interpretability challenges posed through trial and error. A hyperheuristic and reinforcement learning-guided meta-heuristic algorithm recommendation (HHRL-MAR) is proposed to facilitate the adaptive selection of a diverse array of meta-heuristic algorithms tailored to the unique characteristics of various problems in this paper. To this end, four meta-heuristics with distinct advantages are integrated to form the action space within the reinforcement learning, serving as the low-level heuristic for hyperheuristic. The incorporated reward mechanism based on the real-time state of the population enhances both the flexibility and accuracy of the algorithm. Three selection strategies in light of simulated annealing and epsilon - greedy are designed to avoid premature convergence associated with a singular selection approach. The experimental results show the efficacy of HHRL-MAR for large-scale complex continuous optimization in terms of accuracy, stability, and convergence speed.

关键词： reinforcement learning hyperheuristic algorithm automatic algorithm selection meta-heuristic algorithm population activation procedure

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Harmony search meta-heuristic algorithm based on the optimal sizing of wind-battery hybrid micro-grid power system with different battery technologies

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JOURNAL OF ENERGY STORAGE 2024年 75卷

作者： Sun, Xiaohui He, Hongmei Ma, Li Hebei Univ Water Resources & Elect Engn Dept Elect Engn Cangzhou 061001 Hebei Peoples R China

Wind power is a clean and renewable energy source that can contribute to sustainable development. To improve the utilization rate of wind power and reduce the cost of its use in grid-independent areas, this study performs an in-depth evaluation and analysis of the optimal design of wind-battery hybrid micro-grid power schemes with different battery technologies such as Nickel-Iron, Lithium-Ion, and Lead-Acid (LA). First, a mathematical model of a stand-alone wind-battery hybrid micro-grid power scheme is established, to evaluate the overall operation cost and reliability and estimate the optimal design of the micro-grid power scheme with different battery technologies. Then, an improved harmony search meta-heuristic algorithm is presented to optimize the system cost and reliability as the output, which was conceptualized using the musical process. The proposed metaheuristic algorithm is compared using the original harmony search algorithm. Finally, the effects of wind speed, load demands, interest rate, battery lifetime, and the battery's depth of discharge are investigated for the overall operation cost and reliability. Experimental results show that the proposed method is suitable for solving optimal design problems and can reduce the total cost by 13.5 % and can reduce the average simulation time by 1.5 % compared to the original harmony search method. In different scenarios, when an LA battery is selected for the hybrid micro-grid power system;it is suitable for solving the power supply problem and can reduce the total cost and can increase the reliability of the scheme compared to the other case. Finally, system optimization can effectively contribute to the expansion of wind energy use and sustainable development.

关键词： Micro-grid power system Wind power Different batteries Optimal sizing meta-heuristic algorithm

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Modified Social Group Optimization-a meta-heuristic algorithm to solve short-term hydrothermal scheduling

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APPLIED SOFT COMPUTING 2020年 95卷 106524-106524页

作者： Naik, Anima Satapathy, Suresh Chandra Abraham, Ajith KL Univ Dept CSE Hyderabad Telangana India KIIT Deemed Be Univ Sch Comp Engn Bhubaneswar Odisha India MIR Labs Sci Network Innovat & Res Excellence Auburn WA 98071 USA

Social Group Optimization (SGO), developed by Satapathy et al. in the year 2016, is a class of metaheuristic optimization inspired by social behavior. It has two phases: improving phase and acquiring phase. In the improving phase, each individual improves its knowledge by interacting with the best person/solution and in acquiring phase, the individuals interact with randomly selected individuals and the best person simultaneously to acquire knowledge. Modified Social Group Optimization (MSGO) is the improved version of SGO, where the acquiring phase is modified. A self-awareness probability factor is added in the acquiring phase, which enhances the learning capability of an individual from the best-learned person in the societal setup. It is observed that this modification has improved both exploration and exploitation abilities in comparison with the conventional SGO. To analyze the performance of the MSGO, an exhaustive performance comparison is made with GA, PSO, DE, ABC, and a few newer algorithms of the years 2010-2019. The results are tabulated in six experiments. Later, MSGO is applied to solve the short-term hydrothermal scheduling (HTS) problem. The central objective of the HTS problem is to ascertain the optimal plan of action for hydro and thermal generation minimizing the fuel cost of thermal plants and, at the same time satisfying various operational and physical constraints. The valve point loading effect related to the thermal power plants, transmission loss, and other constraints lead HTS as a complex non-linear, non-convex, and non-smooth optimization problem. Simulation results clearly show that the MSGO method is capable of obtaining a better solution. (C) 2020 Elsevier B.V. All rights reserved.

关键词： meta-heuristic algorithm Benchmark functions Fitness function evaluations Social Group Optimization Hydrothermal scheduling problem

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Intelligent accounting optimization method based on meta-heuristic algorithm and CNN

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2281页

作者： Dong, Yanrui Zhengzhou Vocat Coll Finance & Taxat Sch Accounting Zhengzhou Henan Peoples R China

The evolution of social intelligence has led to the adoption of intelligent accounting practices in enterprises. To enhance the efficiency of enterprise accounting operations and improve the capabilities of accountants, we propose an intelligent accounting optimization approach that integrates meta-heuristic algorithms with convolutional neural networks (CNN). First, we enhance the CNN framework by incorporating document and voucher information into accounting audits, creating a multi-modal feature extraction mechanism. Utilizing these multi-modal accounting features, we then introduce a method for assessing accounting quality, which objectively evaluates financial performance. Finally, we propose an optimization technique based on meta- heuristic principles, combining genetic algorithms with annealing models to improve the accounting system. Experimental results validate our approach, demonstrating an accuracy of 0.943 and a mean average precision (mAP) score of 0.812. This method provides technological support for refining accounting audit mechanisms.

关键词： Accounting optimization meta-heuristic algorithm CNN

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PV Cell Parameter Extraction Using Data Prediction-Based meta-heuristic algorithm via Extreme Learning Machine

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Li, Bing Chen, Huang Tan, Tian Shantou Univ Coll Engn Shantou Peoples R China

To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction-based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current-voltage (I-V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction-based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I-V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I-V data;thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.

关键词： parameter extraction PV cell data prediction meta-heuristic algorithm extreme learning machine

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