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State of Health Estimation of Lithium-Ion Battery Using Multi-Health Features Based on Savitzky-Golay Filter and Fitness-Distance Balance- and Lévy Roulette-Enhanced coyote optimization algorithm-Optimized Long Short-Term Memory

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PROCESSES 2024年第10期12卷 2284页

作者： Li, Hongbiao Gao, Dengke Shi, Linlong Zheng, Fei Yang, Bo Shanghai KeLiang Informat Technol Co Ltd Shanghai 201103 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China

Accurate and reliable state of health (SOH) estimation is extremely crucial for the safe and stable operation of lithium-ion batteries (LIBs). In this paper, a method based on L & eacute;vy roulette- and fitness-distance balance-enhanced coyote optimization algorithm-optimized long short-term memory (LRFDBCOA-LSTM) is employed for SOH estimation of LIB. Firstly, six health features are extracted from battery charging and discharging data, and Savitzky-Golay is used to filter the feature data to improve correlation between feature and SOH. Then, L & eacute;vy roulette and fitness-distance balance (FDB) strategies are used to improve the coyote optimization algorithm (COA), i.e., LRFDBCOA. Meanwhile, the improved algorithm is used to optimize the internal parameters of long short-term memory (LSTM) neural network. Finally, the effectiveness of the proposed model is comprehensively validated using five evaluation indicators based on battery data obtained under three different testing conditions. The experimental results manifest that after algorithm improvement and network parameter optimization, the performance of the model is significantly improved. In addition, the method has high estimation accuracy, strong generalization, and strong robustness for SOH estimation with a maximum R2 of 0.9896 and minimum R2 of no less than 0.9711.

关键词： lithium-ion battery state of health Savitzky-Golay coyote optimization algorithm LSTM SimuNPS

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

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

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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

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Chaotic coyote optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第5期13卷 2807-2827页

作者： Tong, Huawei Zhu, Yun Pierezan, Juliano Xu, Youyun Coelho, Leandro dos Santos Nanjing Univ Posts & Telecommun Nanjing 210003 Peoples R China Gannan Normal Univ Ganzhou 341000 Peoples R China Fed Univ Parana UFPR Curitiba Parana Brazil

coyote optimization algorithm (COA) is classified as both swarm intelligence and evolutionary heuristic algorithms. However, getting trapped in a poor local optimum and the low convergence speed are the weaknesses of COA obviously. Due to these weaknesses, this paper proposes a new algorithm named Chaotic coyote optimization algorithm (CCOA) which focusing on COA equipped with chaotic maps. Through utilising ten well-known benchmark functions, experimental results are recorded in tables and drawn in figures to provide a sharp contrast. The performance of CCOA and COA are discussed, which proves CCOA outperforms COA guaranteeing rapid global convergence rate.

关键词： coyote optimization algorithm Chaos theory Swarm intelligence algorithms optimization

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Fuzzy Multilevel Image Thresholding Based on Improved coyote optimization algorithm

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IEEE ACCESS 2021年 9卷 33595-33607页

作者： Li, Linguo Sun, Lijuan Xue, Yu Li, Shujing Huang, Xuwen Mansour, Romany Fouad Nanjing Univ Posts & Telecommun Sch Comp Nanjing 210003 Peoples R China Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Fuyang Normal Univ Coll Comp & Informat Engn Fuyang 236041 Peoples R China New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

Due to the computational complexity of multilevel image thresholding, Swarm Intelligence optimization algorithm (SIOA) has been widely applied to improve the calculation efficiency. Therefore, more and more attention has been paid to exploring the application of the latest SIOA in multilevel segmentation. This article takes Otsu and fuzzy entropy as the objective functions, using coyote optimization algorithm (COA) for multilevel thresholds optimization selection, through fuzzy median aggregation of local neighborhood information and then forms the Fuzzy coyote optimization algorithm (FCOA), so that the thresholding image segmentation can be achieved in the end. To prevent the COA algorithm from falling into the local optimum, this article follows the differential evolution strategy adopted by the standard COA, using the number of iterations to construct the differential scaling factor to form the Improved coyote optimization algorithm (ICOA). The experimental results show that fuzzy Kapur entropy and fuzzy median value aggregation-based ICOA(FICOA) achieves better image segmentation quality. Compared with Grey Wolf Optimizer (GWO), Fuzzy Modified Quick Artificial Bee Colony and Aggregation algorithm (FMQABCA) and Fuzzy Modified Discrete Grey Wolf Optimizer and Aggregation algorithm (FMDGWOA), FCOA and FICOA have certain advantages in visual effects of image segmentation and PSNR, FSIM evaluation indices. Particularly compared with GWO (also a wolf evolutionary algorithm), FICOA shows significant advantages.

关键词： Image segmentation optimization Linear programming Entropy Particle swarm optimization Histograms Heuristic algorithms coyote optimization algorithm information entropy image segmentation multilevel thresholding

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Developing the coyote optimization algorithm for extracting parameters of proton-exchange membrane fuel cell models

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ELECTRICAL ENGINEERING 2021年第1期103卷 563-577页

作者： Sultan, Hamdy M. Menesy, Ahmed S. Kamel, Salah Jurado, Francisco Minia Univ Elect Engn Dept Fac Engn Al Minya 61111 Egypt Moscow Power Engn Inst MPEI Elect Power Syst Dept Moscow 111250 Russia Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China Aswan Univ Dept Elect Engn Aswan 81542 Egypt Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain

Developing a precise semiempirical mathematical model based on multi-nonlinear equations for the proton-exchange membrane fuel cell (PEMFC), which guarantees suitable and accurate simulation of the electrical characteristics of typical PEMFC stacks under various operating scenarios, is the main target of this study. The unknown parameters of the PEMFC model are extracted using a novel efficient optimization technique called coyote optimization algorithm (COA). To validate the effectiveness of the proposed COA-based PEMFC model, two different cases of seven and ten unknown parameters are performed on a commercial PEMFC taken from literature. The sum of squared errors (SSE) between the experimentally measured data and the corresponding computed ones is considered as the objective function. Besides, the effectiveness of the developed algorithm is validated under different operating conditions. Moreover, the results obtained by the application of the proposed COA have been compared with other recent optimization methods reported in the literature, and very competitive results have been provided. Furthermore, parametric and nonparametric statistical analyses are presented to evaluate the accuracy and viability of the developed COA-based PEMFC model.

关键词： Proton-exchange membrane fuel cell Parameters estimation coyote optimization algorithm Sum of squared errors (SSE)

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

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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

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An efficient optimization State-based coyote optimization algorithm and its applications

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Zhang, Qingke Bu, Xianglong Zhan, Zhi-Hui Li, Junqing Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

coyote optimization algorithm (COA) has demonstrated efficient performance by utilizing the multiple pack (subpopulation) mechanism. However, the fixed number of packs and a relatively singular evolutionary strategy limit its comprehensive optimization performance. Thus, this paper proposes a COA variant, referred to as the optimization State-based coyote optimization algorithm (OSCOA). In the OSCOA algorithm, a Population optimization State Estimation Mechanism is employed for estimating the current population optimization state. Then, the estimation result is used to guide the algorithm in setting the number of packs appropriately as well as selecting appropriate evolutionary strategies to refine search directions, thereby avoiding blind exploration. Additionally, the estimation result assists each pack in selecting suitable parents to generate pups, further improving the global search efficiency of the algorithm. To validate the effectiveness of the proposed algorithm, the OSCOA algorithm is subjected to comprehensive testing and analysis along with seven efficient optimizers on 71 benchmark functions derived from the CEC2014, CEC2017, and CEC2022 benchmark suites. The results of these extensive experiments indicate the competitive performance of OSCOA. Furthermore, to further assess the capability of the OSCOA algorithm in addressing real-world problems, two practical applications is considered: wireless sensor network deployment and image segmentation. The outcomes of these applications further confirm the efficacy and stability of the OSCOA algorithm in tackling real-world scenarios.

关键词： Meta-heuristics algorithms coyote optimization algorithm Population state estimation Multi-thresholding image segmentation Deployment problems of wireless sensor networks

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

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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

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Design of Total Harmonic Distortion Reduction Using Quantum coyote optimization algorithm for Hybrid Power Generation Systems

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第17期32卷

作者： Varghese, Lijo Jacob Gandhi Raj, R. Ravi Sankar, R. S. Tan, Zhenhai Sri Krishna Coll Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Univ Coll Engn Dept Elect & Elect Engn BIT Campus Trichy Tamil Nadu India Vignans Inst Informat Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India Nanchang Univ Sch Publ Policy & Management Nanchang Peoples R China

The usage of various inverters in various industries has gained considerable attention in the power electronics industry in recent years. The harmonic distortion that is induced by various renewable energy sources, along with the growing use of nonlinear loads and power electronic devices, has a significant impact on the distribution system. This is because of the rise in penetration of various renewable energy sources and the usage of nonlinear loads and power electronic devices. In addition, distributed generation (DG) units with inverters (such as solar (PV) and wind turbines) that are integrated into distribution networks are viewed as significant harmonic producers that have substantial adverse effects on power quality. The power quality in the system suffers because of these generators. This study proposes a method of harmonic mitigation for addressing power quality problems that are present in distribution systems as a solution to the challenges that have been found. These concerns have been brought to light as a result of previous research. The standard two-level and three-level inverters were the basis for the development of the multilevel inverter (MLI), which was meant to address their shortcomings. One of the effective technologies that can maintain constant performance is the hybrid power generating system. A hybrid energy system has several benefits, including high dependability, cheap cost and minimal emissions. When used in a hybrid power production system, the reduction of total harmonic distortion (THD) becomes absolutely necessary (HPGS). In this context, the research provides a novel approach for high-performance quantum computing called quantum coyote optimization algorithm-based THD reduction (QCOA-THDR). The QCOA-THDR approach that has been presented has been tested with several converters, including SEPIC, buck-boost and Cuk converters. Additionally, the proportional as well as the integral gain variables of the proportional integral (PI) con

关键词： Hybrid power generation PI controller DC to DC converter coyote optimization algorithm total harmonic distortion

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Implementation of coyote optimization algorithm for solving unit commitment problem in power systems

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ENERGY 2023年第PartA期263卷

作者： Ali, E. S. Abd Elazim, S. M. Balobaid, A. S. Jazan Univ Fac Engn Elect Engn Dept Jizan Saudi Arabia Jazan Univ Fac Comp Sci & Informat Technol Comp Sci Dept Jizan Saudi Arabia

The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as coyote optimization algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.

关键词： Generation scheduling Unit commitment coyote optimization algorithm

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