检索结果-内蒙古大学图书馆

Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC model

ENERGY REPORTS 2020年 6卷 1106-1117页

作者： Yuan, Zhi Wang, Weiqing Wang, Haiyun Yildizbasi, Abdullah Xinjiang Univ Minist Educ Engn Res Ctr Renewable Energy Power Generat & Gri Urumqi 830047 Xinjiang Peoples R China Ankara Yildirim Beyazit Univ AYBU Dept Ind Engn TR-06010 Ankara Turkey

In this paper, a new approach has been introduced for optimal parameter estimation of a proton exchange membrane fuel cell (PEMFC) model. The main purpose is to minimize the total error between the empirical data and the proposed method by optimal parameter selection of the model. The methodology is based on using a newly introduced developed version of the coyote optimization algorithm (DCOA) for determining the value of the unknown parameters in the model. Two different PEMFC models including 2 kW Nexa FC and 6kW NedSstack PS6 FC are adopted for validation and the results are compared with the empirical data and some well-known methods including conventional COA, Seagull optimization algorithm, and (N + lambda) - ES algorithm to show the proposed method's superiority toward the literature methods. The final results declared a satisfying agreement between the proposed DCOA and the empirical data. The results also declared the excellence of the presented method toward the other compared methods. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Proton exchange membrane fuel coyote optimization algorithm Developed Model parameter estimation Electrical circuit equivalent

来源：评论

学校读者我要写书评

暂无评论

Improved coyote optimization algorithm and Deep Learning Driven Activity Recognition in Healthcare

引用

IEEE ACCESS 2024年 12卷 22158-22166页

作者： Alazwari, Sana Eltahir, Majdy M. Almalki, Nabil Sharaf Alzahrani, Abdulrahman Alnfiai, Mrim M. Salama, Ahmed S. Taif Univ Coll Comp & Informat Technol Dept Informat Technol Taif 21944 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Informat Syst Abha 61421 Saudi Arabia King Saud Univ Coll Educ Dept Special Educ Riyadh 12372 Saudi Arabia Univ Hafr Al Batin Coll Comp Sci & Engn Dept Comp Sci & Engn Hafar al Batin 39524 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt

Healthcare is an area of concern where the application of human-centred design practices and principles can enormously affect well-being and patient care. The provision of high-quality healthcare services requires a deep understanding of patients' needs, experiences, and preferences. Human activity recognition (HAR) is paramount in healthcare monitoring by using machine learning (ML), sensor data, and artificial intelligence (AI) to track and discern individuals' behaviours and physical movements. This technology allows healthcare professionals to remotely monitor patients, thereby ensuring they adhere to prescribed rehabilitation or exercise routines, and identify falls or anomalies, improving overall care and safety of the patient. HAR for healthcare monitoring, driven by deep learning (DL) algorithms, leverages neural networks and large quantities of sensor information to autonomously and accurately detect and track patients' behaviors and physical activities. DL-based HAR provides a cutting-edge solution for healthcare professionals to provide precise and more proactive interventions, reducing the burden on healthcare systems and improving patient well-being while increasing the overall quality of care. Therefore, the study presents an improved coyote optimization algorithm with a deep learning-assisted HAR (ICOADL-HAR) approach for healthcare monitoring. The purpose of the ICOADL-HAR technique is to analyze the sensor information of the patients to determine the different kinds of activities. In the primary stage, the ICOADL-HAR model allows a data normalization process using the Z-score approach. For activity recognition, the ICOADL-HAR technique employs an attention-based long short-term memory (ALSTM) model. Finally, the hyperparameter tuning of the ALSTM model can be performed by using ICOA. The stimulation validation of the ICOADL-HAR model takes place using benchmark HAR datasets. The wide-ranging comparison analysis highlighted the improved recognition r

关键词： Human activity recognition hyperparameter tuning coyote optimization algorithm wearable sensor deep learning

来源：评论

学校读者我要写书评

暂无评论

Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2021年第9期33卷 4343-4371页

作者： Thai Dinh Pham Thang Trung Nguyen Bach Hoang Dinh Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Power Syst Optimizat Res Grp Ho Chi Minh City Vietnam

This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SS

关键词： coyote optimization algorithm Distributed generation Real power loss Operation cost

来源：评论

学校读者我要写书评

暂无评论

Improved coyote optimization algorithm for parameter estimation of lithium-ion batteries

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2023年第4期237卷 787-796页

作者： Hao, Yuefei Ding, Jie Huang, Shimeng Xiao, Min Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence 9 Wenyuan Rd Nanjing 210023 Peoples R China

This paper studies the parameter estimation of fractional order equivalent circuit model of lithium-ion batteries. Since intelligent optimization algorithms can achieve parameters with high accuracy by transforming the parameter estimation into optimization problem, coyote optimization algorithm is taken in this paper by modifying two key steps so as to improve the accuracy and convergence speed. Firstly, tent chaotic map is introduced to avoid falling into local optimum and enhance population diversity. Secondly, dual strategy learning is employed to improve the searching ability, accuracy and convergence speed. Non-parametric statistical significance is tested by 6 benchmark functions with the comparison of other 5 optimization algorithms. Furthermore, the proposed algorithm is applied to identify the fractional order model of the Samsung ICR18650 (2600 mAh) and compared with conventional coyote optimization algorithm and particle swarm algorithm, which declared the excellence in identification accuracy.

关键词： Fractional order model lithium-ion battery coyote optimization algorithm parameter identification

来源：评论

学校读者我要写书评

暂无评论

A modified variant of coyote optimization algorithm for solving ordinary differential equations and oscillatory mechanical problems

引用

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2022年第12期98卷 1161-1178页

作者： Eid, Heba F. Mansour, Romany F. Cuevas, Erik Al Azhar Univ Fac Sci Math & Comp Dept Cairo 11651 Egypt New Valley Univ Fac Sci Dept Math Kharga New Valley Gove Egypt Univ Guadalajara Dept Elect Guadalajara Jalisco Mexico

A vital subject of engineering structures is mechanical oscillations. If the mechanical oscillation is uncontrolled, it can lead to structural failure due to large dynamic stresses developed, as the collapse occurred on the Broughton Suspension bridge due to soldiers walking in step. This paper addresses mechanical oscillation problems employing a proposed modified variant of coyote optimizer. coyote optimization algorithm (COA) is a new meta-heuristic which mimics the social behavior of coyotes. COA suffers with stagnation problems and immature convergence while solving optimization problems. In this paper, the COA is hybridized with Laplace Crossover operator and new culture tendency strategies are adapted, modified variants of coyote optimization algorithm (MvCOA). The proposed MvCOA is presented to approximately solve mechanical oscillation problems independently of their order, form, and stated conditions. With the fundamental concepts of ordinary differential equations and Fourier series expansion, mechanical oscillation problems can be modeled as a problem of optimization whereby the optimization task is achieved using the proposed MvCOA. Five different ordinary differential equations and four mechanical oscillation problems are solved approximately and then compared with their corresponding exact solutions. The statistical analysis validates that the presented MvCOA is an effective algorithm for different optimization problems. However, from the empirical results, it is visible that the suggested MvCOA approximate approach was able to reach a successful performance solving different mechanical oscillation problems.

关键词： Bio-inspired computing coyote optimization algorithm mechanical oscillations ordinary differential equations approximate solution

来源：评论

学校读者我要写书评

暂无评论

New optimized configuration for a hybrid PV/diesel/battery system based on coyote optimization algorithm: A case study for Hotan county

引用

ENERGY REPORTS 2022年 8卷 15480-15492页

作者： Sari, Arif Majdi, Ali Opulencia, Maria Jade Catalan Timoshin, Anton Dinh Tran Ngoc Huy Nguyen Dinh Trung Alsaikhan, Fahad Hammid, Ali Thaeer Akhmedov, Abdulaziz Girne Amer Univ Dept Management Informat Syst Via Mersin 10 Kyrenia North Cyprus Turkey Al Mustaqbal Univ Coll Dept Bldg & Construct Tech Hilla Iraq Ajman Univ Coll Business Adm Ajman U Arab Emirates Sechenov Univ IM Sechenov First Moscow State Med Univ Dept Propaedeut Dent Dis Moscow Russia Banking Univ HCMC Ho Chi Minh City Ho Chi Minh City Vietnam Int Univ Japan Niigata Japan NEU Hanoi Vietnam Prince Sattam Bin Abdulaziz Univ Clin Pharm Dept Clin Pharm Coll Pharm Alkharj Saudi Arabia Imam Jaafar Al Sadiq Univ Dept Comp Engn Baghdad Iraq Dept Informat & Teaching Methods Bunyodkor St 27 Tashkent Uzbekistan

The main objective of this study is to present a multi-objective and optimal hybrid PV/diesel generator/battery Renewable Energy System (HRES) to provide this reliability in the Hotan county, placed in Taklamakan Desert. This study uses the epsilon-constraint method along with a developed version of the coyote optimization algorithm to achieve the best values of the component sized to decrease the loss of load probability, CO2 emission value, and the annualized cost of the system. Sensitivity analysis also is performed to show each component's impact on the system. The results demonstrate that the DG backup system improves the yearly cost of the system from 8347.2 $ to 9318.4 $, which shows about 10.42% increasing by increasing the fuel consumption. Here, the LLP increases from 0% to 9.19% and the CO2 emissions improve from 2531.2 kg/yr to 13257 kg/yr. Accordingly, the COE value is reduced from 0.39 $/kWh to 0.24 $/kWh over the PV penetration, reducing from 92.27% to 59.42%. This decreasing indicates that the system fuel cost has more impact than the cost of PV on the COE, which is due to the low cost required of conventional power production than the PV system. The results also indicate a noteworthy upshot on the battery storage unit size such that the size of epsilon(CO2) has been enhanced from 27.4 kWh to 50 kWh in the range from 7000 kg/year to 25 kg/year. The results also are compared with the PSO-based optimal system and HOMER software results to show its excellence toward them. (c) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Hybrid renewable energy system Multi-objective optimization Taklamakan desert epsilon-constraint method Sensitivity analysis HOMER coyote optimization algorithm Improved

来源：评论

学校读者我要写书评

暂无评论

coyote optimization algorithm for optimal allocation of interline-Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration

引用

JOURNAL OF ENERGY STORAGE 2021年 41卷

作者： Janamala, Varaprasad Reddy, D. Sreenivasulu CHRIST Deemed Univ Sch Engn & Technol Dept Elect & Elect Engn Bangalore 560074 Karnataka India

In current times, there is a need to do power system planning to endure situations of any kind. An islanding operation is one such unavoidable situation that may be required in many cases for both technical and economic reasons. First and foremost, this paper focuses on the determination of the best allotment of Interline-Photovoltaic (I-PV) system as per Electric Vehicle (EV) load penetration in the network. With different operational constraints, a multi-objective optimization using real power loss and voltage deviation index is formulated and solved using the coyote optimization algorithm (COA). The paper highlights the computational efficiency of COA with Particle Swarm optimization (PSO) and Grey Wolf Optimizer (GWO), in addition to various literary works, and the results suggest the superiority of COA by its global optima. The required battery energy storage system (BESS) capacity for supplying an islanded network's entire load demand for a day is determined in the second stage. The simulations were carried out on the IEEE 33-bus electrical distribution network (EDN) contemplating different levels of EV load penetration. The proposed methodology results have proved that the required energy is provided by optimal I-PV-BESS backup for a daylong islanding operation and its adaptability for practical situations.

关键词： Battery energy storage system coyote optimization algorithm Electric vehicle load Islanding operation Multi-objective optimization Solar photovoltaic system

来源：评论

学校读者我要写书评

暂无评论

The novel multi-swarm coyote optimization algorithm for automatic skin lesion segmentation

引用

EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 679-711页

作者： Sayed, Gehad Ismail Khoriba, Ghada Haggag, Mohamed H. Cairo Univ Fac Comp & Informat Giza Egypt Helwan Univ Fac Comp & Informat Helwan Egypt

coyote optimization algorithm (COA) is one of population-based swarm intelligence algorithms inspired by the swarming behavior of coyotes. However, COA showed its effectiveness in solving the global optimization problem, it suffers from premature convergence and stagnation in local optima, espicially in a complex space. In this paper, the multi-swarm topology is employed, where the population is divided into several sub-swarms. The performance of multi-swarm coyote optimization algorithm (MCOA) is evaluated on a set of benchmark functions provided in the IEEE CEC 2005 and IEEE CEC 2017 special sessions. Also, it is evaluated for solving multi-level thresholding problem, where 44 skin dermoscopic images obatined from PH2 benchmark dataset are used. The experimental results showed that employing mutli-swarm topology can significantly improve the population diversity and thus the exploration ability. Also, the results reveal that proposed MCOA has the advantages of remarkable stability and high accuracy compared with its classical version and other state-of-art meta-heuristic optimization algorithms. Additionally, a new skin lesion segmentation model based on MCOA is proposed as well. The results illustrate the effectiveness and efficiency of the proposed model and it can be further used for skin disease diagnosis and treatment planning.

关键词： coyote optimization algorithm Multilevel thresholding Global optimization PH2 dataset

来源：评论

学校读者我要写书评

暂无评论

A coyote optimization algorithm-trained Dendritic Neuron Model for Prediction Tasks 12

A Coyote Optimization Algorithm-trained Dendritic Neuron Mod...

引用

Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： He, Houtian Xue, Chenxi Li, Qianqian Gao, Shangce Univ Toyama Fac Engn Toyama Japan

ISBN: (纸本)9781665499248

Time series prediction is always a hot topic. Many artificial neural network-based models are continuously proposed to improve the predicting performance. However, in practical situations, the large scale of training data and the complex structure of network often lead to slow training speed and unsatisfactory result. Thus, it is reasonable to use a suitable algorithm to train a well-structured network for such an issue. In this paper, we use the coyote optimization algorithm to train a dendritic neuron and propose the COADNM to tackle time series prediction problems. Three datasets are used to verify the performance of the proposed model, meanwhile, four other neural network-based models are employed to compare with COADNM. The experiment result shows that our model outperforms all its competitors via different evaluation criteria.

关键词： Dendritic neuron model coyote optimization algorithm time series prediction

来源：评论

学校读者我要写书评

暂无评论

Economic Dispatch Integrated Wind Power Using coyote optimization algorithm 7

Economic Dispatch Integrated Wind Power Using Coyote Optimiz...

引用

7th International Istanbul Smart Grids and Cities Congress and Fair (ICSG)

作者： Guvenc, Ugur Kaymaz, Enes Duzce Univ Elect & Elect Engn Duzce Turkey

ISBN: (纸本)9781728113159

Fossil fuels used in power system cause air pollution and global warming because of releasing greenhouse gases. Nowadays, renewable energy especially wind power has more widespread in power generation due to ecological concerns and increasing fuel prices. Therefore, it is presented the Economic Dispatch integrated wind power approach in this paper. However, wind power is stochastic because wind speed is uncertain in nature. Therefore Weibull Probability Density Function ( PDF) and Incomplete Gamma Function (IGF) are used to estimating and modelling wind power. To solve the problem effectively, coyote optimization algorithm (COA) is implemented to the problem and it was tested on various power system consisting thermal generator and wind power generator. Simulation results generated by COA are compared with other heuristic algorithm such as GA and PSO. It can be clearly seen that COA produces better results than GA and PSO.

关键词： economic dispatch coyote optimization algorithm wind power renewable energy

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：