检索结果-内蒙古大学图书馆

cooperation search algorithm for optimal parameters identification of SOFCs feeding electric vehicle at steady and dynamic modes

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartB期50卷 1395-1407页

作者： Ismael, Islam El-Fergany, Attia A. Gouda, Eid A. Kotb, Mohamed F. Mansoura Univ Fac Engn Dept Elect Engn Mansoura Egypt Zagazig Univ Fac Engn Dept Elect Power & Machines Zagazig 44519 Egypt

In this work, the new application of cooperation search algorithm (CSA) is addressed to extract the unknown solid oxide fuel cell (SOFC)'s parameters supported by investigating and testing the cell steady-state and dynamic performances. Primarily, the indefinite SOFC's parameters are estimated utilizing CSA and the results are assessed and verified with compulsory comparisons. The minimum errors are obtained, CSA convergence behavior and SOFC's performance are all compared with other recognized algorithms in the literature. At last, and for more validations, the dynamic behavior of the SOFCs is studied when supplying electric vehicle (EV) operated with four DC motors at different speeds. The SOFCs and EV are represented via Simulink/MATLAB package. The EV's input current, voltage, power, and torque are traced at various acceleration and speed according to the road nature and/or driver requirements. The extensive investigations and validations prove the high efficacy of CSA capabilities in representing the SOFCs and for analyzing their steady-state and dynamic behaviors. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cells Steady-state model Dynamic performance Optimization methods cooperation search algorithm Electric vehicle

来源：评论

学校读者我要写书评

暂无评论

Twin extreme learning machine model and cooperation search algorithm for multi-step-ahead point and interval runoff prediction

引用

JOURNAL OF HYDROLOGY 2025年 653卷

作者： Feng, Zhong-kai Liu, Pan Niu, Wen-jing Fu, Xin-yue Xiao, Yang Yang, Tao Huang, Hai-yan Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Suzhou Univ Sci & Technol Sch Environm Sci & Engn Suzhou 215009 Peoples R China Yunnan Water Resources & Hydropower Vocat Coll Kunming 650051 Peoples R China

Accurate runoff predictions provide crucial technical supporting information for water resource decision-makers, offering insights into future runoff changes. This study investigates the effectiveness of twin extreme learning machine (TELM) and cooperation search algorithm (CSA) in multi-step-ahead point and interval runoff prediction. Then, three multi-step-ahead forecasting strategies are considered to develop various models: recursive, direct, and direct-recursive. The results show that the developed model consistently delivers superior accuracy and reliability in predicting runoff, while CSA outperforms other evolutionary methods in determining model parameters. However, no single forecasting strategy consistently outshines others across all scenarios, with the recursive strategy showing a slight edge in performance. Besides, the interval runoff predictions confirm the effectiveness of TELM in yielding high-quality prediction intervals across various experiments by incorporating upper and lower boundary estimation and boundary functions. For station A with a 98% confidence level, the proposed method achieves prediction interval coverage probability, prediction interval normalized average width, and coverage width criterion of 0.9904, 0.1138, and 0.6828, respectively, indicating overall high interval prediction quality. Thus, a novel artificial intelligence model is developed for multi-step-ahead point and interval runoff prediction.

关键词： Multi-step-ahead runoff forecasting cooperation search algorithm Twin extreme learning machine Point prediction Interval prediction Hydrological time series forecasting

来源：评论

学校读者我要写书评

暂无评论

A novel opposition-based hybrid cooperation search algorithm with Nelder-Mead for tuning of FOPID-controlled buck converter

引用

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第10期46卷 1924-1942页

作者： Ersali, Cihan Hekimoglu, Baran Batman Univ Dept Elect & Elect Engn TR-72100 Batman Turkiye

This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder-Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder-Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional-integral-derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm's performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Levy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter's switched circuit with the OCSANM-based FOPID controller further underscores the algorithm's effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach's time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems.

关键词： cooperation search algorithm opposition-based learning Nelder-Mead simplex search FOPID controller buck converter

来源：评论

学校读者我要写书评

暂无评论

Optimization Task Scheduling Using cooperation search algorithm for Heterogeneous Cloud Computing Systems

引用

Computers, Materials & Continua 2023年第1期74卷 2133-2148页

作者： Ahmed Y.Hamed M.Kh.Elnahary Faisal S.Alsubaei Hamdy H.El-Sayed Department of Computer Science Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the *** a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud *** primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence *** and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor *** diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing *** a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the *** research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing *** basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal *** assess our algorithm’s performance by running it through three scenarios with varying numbers of *** findings demonstrate that the suggested technique beats existingmethods NewGenetic algorithm(NGA),Genetic algorithm(GA),Whale Optimization algorithm(WOA),Gravitational search algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

关键词： Heterogeneous processors cooperation search algorithm task scheduling cloud computing

来源：评论

学校读者我要写书评

暂无评论

cooperation search algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs

引用

WATER RESOURCES MANAGEMENT 2021年第8期35卷 2465-2485页

作者： Niu, Wen-jing Feng, Zhong-kai Li, Yu-rong Liu, Shuai ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China China Water Resources Beifang Invest Design & Res Tianjin 300222 Peoples R China

Over the past decades, the reservoirs operation plays an increasingly important role in coping with the serious water, food and energy crisis. However, the curse of dimensionality poses huge challenges for operators because the computation cost often grows exponentially with the expansion of hydropower system. With strong search performance and high execution efficiency, metaheuristic search algorithms become the research hotspot in the reservoir operation field. Here, cooperation search algorithm (CSA) inspired by team cooperation behaviors in modern enterprise is introduced for power generation operation of cascade hydropower reservoirs. In CSA, the team communication operator is designed to determine promising search areas, and then the reflective learning operator is adopted to enhance the local search, while the internal competition operator is used to retain the elite individuals. The feasibility of the CSA method in numerical optimization problems is fully proved by the simulation results of several test functions. Then, the CSA method is used to solve the power generation operation of cascade hydropower reservoirs. The results show that the CSA method outperforms several traditional methods in both convergence rate and search precision. Thus, an effective tool with strong reliability and robustness is provided for the complex reservoir operation problem.

关键词： Cascade hydropower reservoirs Optimal operation cooperation search algorithm Metaheuristic evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-objective cooperation search algorithm based on decomposition for complex engineering optimization and reservoir operation problems

引用

APPLIED SOFT COMPUTING 2024年 167卷

作者： Yao, Xin-ru Feng, Zhong-kai Zhang, Li Niu, Wen-jing Yang, Tao Xiao, Yang Tang, Hong-wu Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Hohai Univ Key Lab Soil & Water Proc Watershed Nanjing 210098 Peoples R China Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Suzhou Univ Sci & Technol Sch Environm Sci & Engn Suzhou 215009 Peoples R China

This study introduces a novel multi-objective cooperation search algorithm based on decomposition (MOCSA/D) to address multi-objective competitive challenges in engineering problem. Inspired by the optimization strategy of single-objective cooperation search algorithm (CSA) and the decomposition framework of MOEA/D, MOCSA/ D algorithm randomly generates initial solutions in the optimization space, and then repeatedly executes four search strategies until the end of iteration: Cooperative updating strategy gathers high-quality information to update solutions with balanced distribution. Reflective adjustment strategy expands the exploration range of the population, enabling the acquisition of solutions with strong optimization capabilities. Internal competition strategy selects superior individuals with better performance for subsequent optimization. Density updating strategy improves the competitiveness of optimized individuals within the population, fostering a more diverse solution set. Three numerical experiments (including DTLZ, WFG unconstrained test problems, ZXH_CF constrained test problems and RWMOP real-world multi-objective optimization problems) are tested to further comprehensively evaluate the dominant performance of MOCSA/D. The test results in different problem scenarios show that compared with the existing excellent evolutionary algorithms, MOCSA/D can always obtain a better, stable and uniform distribution of non-dominated solutions, and has higher solving efficiency and optimization quality under different performance evaluation metrics with the increasing difficulty of solving problems. Finally, the proposed algorithm is applied to the multi-objective reservoir engineering optimization problem to verify the feasibility of the decision scheme and the comprehensive benefit optimization of MOCSA/ D. Overall, MOCSA/D can simplify the problem optimization difficulty based on decomposition mechanism, and improve the global optimization of population, pat

关键词： cooperation search algorithm Engineering optimization Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Short-term electricity load time series prediction by machine learning model via feature selection and parameter optimization using hybrid cooperation search algorithm

引用

ENVIRONMENTAL REsearch LETTERS 2021年第5期16卷 055032-055032页

作者： Niu, Wen-jing Feng, Zhong-kai Li, Shu-shan Wu, Hui-jun Wang, Jia-yang ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China China Southern Power Grid Power Dispatching Control Ctr Guangzhou 510663 Peoples R China China Southern Power Grid Power Res Inst Guangzhou 510080 Peoples R China

Reliable load time series forecasting plays an important role in guaranteeing the safe and stable operation of modern power system. Due to the volatility and randomness of electricity demand, the conventional forecasting method may fail to effectively capture the dynamic change of load curves. To satisfy this practical necessity, the goal of this paper is set to develop a practical machine learning model based on feature selection and parameter optimization for short-term load prediction. In the proposed model, the ensemble empirical mode decomposition is used to divide the original loads into a sequence of relatively simple subcomponents;for each subcomponent, the support vector machine is chosen as the basic predictor where the real-valued cooperation search algorithm (CSA) is used to seek the best model hyperparameters, while the binary-valued CSA is set as the feature selection tool to determine the candidate input variables;finally, the aggregation of all the submodules' outputs forms the final forecasting result. The presented method is assessed by short-term load data from four provincial-grid dispatching centers in China. The experiments demonstrate that the proposed model can provide better results than several conventional models in short-term load prediction, while the emerging CSA method is an effective tool to determine the parameter combinations of machine learning method.

关键词： load prediction support vector machine cooperation search algorithm parameter optimization feature selection artificial intelligence machine learning

来源：评论

学校读者我要写书评

暂无评论

Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction

引用

JOURNAL OF HYDROLOGY 2021年 595卷 126062-126062页

作者： Feng, Zhong-kai Niu, Wen-jing Tang, Zheng-yang Xu, Yang Zhang, Hai-rong Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China China Yangtze Power Co Ltd Dept Water Resources Management Yichang 443133 Peoples R China Hubei Key Lab Intelligent Yangtze & Hydroelect Sc Yichang 443133 Peoples R China

Reliable and stable hydrological prediction plays a vitally crucial role in the scientific operation of water resources system. As a famous artificial intelligence method for hydrological forecasting, extreme learning machine (ELM) has the virtues of fast training efficiency and strong generalization performance but is easily trapped into local optima because the preset computation parameters often remain unchanged in the learning process. In order to overcome this shortcoming, a practical evolutionary artificial intelligence model is developed for multiple scales nonstationary hydrological time series prediction. In the proposed method, an emerging evolutionary method called cooperation search algorithm (CSA) is used to search for the optimal input-hidden weights and hidden biases of the ELM model for the first time. The proposed method is used to forecast the runoff time series of three real-world hydrological stations in China. The experimental results show that the CSA approach can effectively determine satisfying network parameters of the ELM model, while our method can produce better results than the traditional ELM method in terms of all the performance evaluation indexes. Taking 1-step-ahead runoff forecasting at station B as an example, our method betters the ELM method with 15.76% and 42.35% improvements in both root mean squared error and mean absolute percentage error at the testing phase. Thus, a novel multiscale nonstationary hydrological prediction tool is developed to support the decision-making of water resource system.

关键词： Hydrological forecasting Extreme learning machine cooperation search algorithm Artificial intelligence Machine learning Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Gaussian Process Regression and cooperation search algorithm for Forecasting Nonstationary Runoff Time Series

引用

WATER 2023年第11期15卷 2111页

作者： Wang, Sen Gong, Jintai Gao, Haoyu Liu, Wenjie Feng, Zhongkai Pearl River Water Resources Res Inst Guangzhou 510610 Peoples R China Minist Water Resources Key Lab Pearl River Estuary Regulat & Protect Guangzhou 510610 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Minist Water Resources Key Lab Water Secur Guarantee Guangdong Hong Kong Hong Kong 510610 Peoples R China

In the hydrology field, hydrological forecasting is regarded as one of the most challenging engineering tasks, as runoff has significant spatial-temporal variability under the influences of multiple physical factors from both climate events and human activities. As a well-known artificial intelligence tool, Gaussian process regression (GPR) possesses satisfying generalization performance but often suffers from local convergence and sensitivity to initial conditions in practice. To enhance its performance, this paper investigates the effectiveness of a hybrid GPR and cooperation search algorithm (CSA) model for forecasting nonstationary hydrological data series. The CSA approach avoids the premature convergence defect in GPR by effectively determining suitable parameter combinations in the problem space. Several traditional machine learning models are established to evaluate the validity of the proposed GPR-CSA method in three real-world hydrological stations of China. In the modeling process, statistical characteristics and expert knowledge are used to select input variables from the observed runoff data at previous periods. Different experimental results show that the developed GPR-CSA model can accurately predict nonlinear runoff and outperforms the developed traditional models in terms of various statistical indicators. Hence, a CSA-trained GPR model can provide satisfying training efficiency and robust simulation performance for runoff forecasting.

关键词： streamflow prediction Gaussian process regression cooperation search algorithm artificial intelligence machine learning

来源：评论

学校读者我要写书评

暂无评论

cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems

引用

APPLIED SOFT COMPUTING 2021年 98卷 106734-106734页

作者： Feng, Zhong-kai Niu, Wen-jing Liu, Shuai Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China China Water Resources Beifang Invest Design & Res Tianjin 300222 Peoples R China

This paper develops a novel population-based evolutionary method called cooperation search algorithm (CSA) to address the complex global optimization problem. Inspired by the team cooperation behaviors in modern enterprise, the CSA method randomly generates a set of candidate solutions in the problem space, and then three operators are repeatedly executed until the stopping criterion is met: the team communication operator is used to improve the global exploration and determine the promising search area;the reflective learning operator is used to achieve a comprise between exploration and exploitation;the internal competition operator is used to choose solutions with better performances for the next cycle. Firstly, three kinds of mathematical optimization problems (including 24 famous test functions, 25 CEC2005 test problems and 30 CEC2014 test problems) are used to test the convergence speed and search accuracy of the CSA method. Then, several famous engineering optimization problems (like Gear train design, Welded beam design and Speed reducer design) are chosen to testify the engineering practicality of the CSA method. The results in different scenarios demonstrate that as compared with several existing evolutionary algorithms, the CSA method can effectively explore the decision space and produce competitive results in terms of various performance evaluation indicators. Thus, an effective tool is provided for solving the complex global optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Numerical optimization Engineering optimization Population-based metaheuristic method cooperation search algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：