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2024 International Conference on Energy Technology and Electrical Power, ETEP 2024

作者： Liang, Weichen Ma, Xinsheng Li, Xuan Zhang, Xinyue North China Electric Power Research Institute Co. Ltd. Electric Research Building No.1 Dizang An South Lane Xicheng District Beijing Beijing 100032 China North China Electric Power University No.2 Beinong Road Huilongguan Changping District Beijing Beijing 102200 China

ISBN: (纸本)9781510690400

As a crucial component of the power system, the distribution network undertakes the core tasks of power transmission and distribution, and plays an indispensable role in the operation and stable power supply of the power system. Optimizing the installation location of distribution automation terminals aims to balance the reliability and economy of power supply in the distribution network. It is a key strategy to improve power supply quality and ensure the safe and stable operation of the distribution network. It is also an important issue that urgently needs to be addressed in the construction of the distribution network. A mutation opposition-based learning based coyote optimization algorithm is proposed to solve the optimization layout problem of distribution automation terminals. Firstly, taking the minimum annual comprehensive cost as the objective function, considering reliability constraints and other conditions, the optimal number of terminal arrangements is determined;then, based on the mutation opposition-based learning based coyote optimization algorithm, the optimal layout position of the terminal is calculated. This paper takes the actual distribution network as an example to verify that the proposed method can effectively reduce costs and further determine the optimal layout of terminals. © 2025 SPIE.

关键词： coyote optimization algorithm distribution automation opposition-based learning terminal layout

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coyote optimization algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

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IEEE ACCESS 2020年 8卷 111102-111140页

作者： Diab, Ahmed A. Zaki Sultan, Hamdy M. Ton Duc Do Kamel, Omar Makram Mossa, Mahmoud A. Minia Univ Fac Engn Elect Engn Dept Al Minya 61111 Egypt Kyushu Univ Dept Elect & Elect Engn Fukuoka 8190395 Japan Nazarbayev Univ Sch Engn & Digital Sci SEDS Dept Robot & Mechatron Z05H0P9 Nur Sultan Kazakhstan El Minia High Inst Engn & Technol Elect & Comp Dept Al Minya 61125 Egypt Moscow Power Engn Inst MPEI Elect Power Syst Dept Moscow 111250 Russia

Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, coyote optimization algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of theCOAalgorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547 x 10(-4), 7.64801 x 10(-4), and 7.59756 x 10(-4) for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

关键词： Mathematical model Integrated circuit modeling optimization Photovoltaic cells Analytical models Parameter estimation Estimation Solar cells PV modules parameter extraction optimization coyote optimization algorithm single diode model double diode model three diode model

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coyote optimization algorithm for the parameter extraction of photovoltaic cells

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SOLAR ENERGY 2019年 194卷 656-670页

作者： Chin, Vun Jack Salam, Zainal Univ Southampton Malaysia Sch Elect & Comp Sci Iskandar Puteri 79200 Malaysia Univ Teknol Malaysia Sch Elect Engn Ctr Elect Energy Syst Johor Baharu 81310 Malaysia Univ Teknol Malaysia Inst Future Energy Johor Baharu 81310 Malaysia

In this paper, a new and powerful metaheuristic optimization technique known as the coyote optimization algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms adopted by the coyotes to ensure the survivability of their species, the COA possesses several outstanding merits such as low number of control parameters, ease of implementation and diverse mechanisms for balancing exploration and exploitation. For physically meaningful solutions, a set of parametric constraints is introduced to prevent the coyotes from straying outside of the predefined boundaries of the search space. Extensive tests indicate that the proposed optimizer exhibits superior accuracy compared to other state-of-the-art EA-based parameter extraction methods. It achieved root-mean-square error (RSME) as low as 7.7301E-04 A and 7.3265E-04 A, for the single-diode and two-diode models, respectively. Moreover, the algorithm maintains outstanding performance when tested on an assortment of modules of different technologies (i.e. mono-crystalline, poly-crystalline, and thin film) at varying irradiance and temperature. The standard deviations (STDs) of the fitness values over 35 runs are measured to be less than 1 x 10(-5) for both models. This suggests that the results produced by the algorithm are highly consistent. With these outstanding merits, the COA is envisaged to be a competitive option for the parameter extraction problem of PV cell/module.

关键词： Solar photovoltaic Parameter extraction coyote optimization algorithm Evolutionary algorithm Equivalent circuit model

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coyote optimization algorithm with Linear Convergence for Global Numerical optimization 9th

Coyote Optimization Algorithm with Linear Convergence for Gl...

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9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM)

作者： Lin, Hsin-Jui Hsieh, Sheng-Ta Asia Eastern Univ Sci & Technol New Taipei Taiwan

ISBN: (纸本)9783030980184;9783030980177

In particular, the popularity of computational intelligence has accelerated the study of optimization. coyote optimization algorithm (COA) is a new meta heuristic optimization. It is pays attention to the social structure and experience exchange of coyotes. In this paper, the coyote optimization algorithm with linear convergence (COALC) is proposed. In order to explore a huge search space in the pre-optimization stage and to avoid premature convergence, the convergence factor is also involved. Thus, the COALC will explore a huge search space in the early optimization stage to avoid premature convergence. Also, the small area is adopted in the later optimization stage to effectively refine the final solution, while simulating a coyote killed by a hunter in the environment. It can avoid the influence of bad solutions. In experiments, ten IEEE CEC2019 test functions is adopted. The results show that the proposed method has rapid convergence, and a better solution can be obtained in a limited time, so it has advantages compared with other related methods.

关键词： Functional optimization Swarm intelligence Global optimization problems coyote optimization algorithm coyote optimization algorithm with linear convergence

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coyote optimization algorithm: A new metaheuristic for global optimization problems

Coyote Optimization Algorithm: A new metaheuristic for globa...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pierezan, Juliano Coelho, Leandro dos Santos Fed Univ Parana UFPR Dept Elect Engn Lactec Inst LACTEC Dept Mech Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Polytech Sch Curitiba Parana Brazil

ISBN: (纸本)9781509060177

The behavior of natural phenomena has become one of the most popular sources for researchers to design optimization algorithms for scientific, computing and engineering fields. As a result, a lot of nature-inspired algorithms have been proposed in the last decades. Due to the numerous issues of the global optimization process, new algorithms are always welcome in this research field. This paper introduces the coyote optimization algorithm (COA), which is a population based metaheuristic for optimization inspired on the canis latrans species. It contributes with a new algorithmic structure and mechanisms for balancing exploration and exploitation. A set of boundary constrained real parameter optimization benchmarks is tested and a comparative study with other nature-inspired metaheuristics is provided to investigate the performance of the COA. Numerical results and non-parametric statistical significance tests indicate that the COA is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.

关键词： Global optimization Bio-inspired metaheuristics coyote optimization algorithm

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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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Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using coyote optimization algorithm

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IEEE ACCESS 2022年 10卷 52744-52759页

作者： Taleb, Sylia Mekhmoukh Meraihi, Yassine Gabis, Asma Benmessaoud Mirjalili, Seyedali Zaguia, Atef Ramdane-Cherif, Amar Univ MHamed Bougara Boumerdes LIST Lab Boumerdes 35000 Algeria Lab Methodes Concept Syst Algiers 16309 Algeria Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab YFL Seoul 03722 South Korea Taif Univ Coll Comp & Informat Technol Dept Comp Sci At Taif 21944 Saudi Arabia Univ Versailles St Quentin En Yvelines LISV Lab F-78140 Velizy Villacoublay France

Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network operators and influences strongly the WMNs performance. This challenging issue is known to be an NP-hard problem, and solving it using approximate optimization algorithms (i.e. heuristic and meta-heuristic) is essential. This motivates our attempts to present an application of the coyote optimization algorithm (COA) to solve the mesh routers placement problem in WMNs in this work. Experiments are conducted on several scenarios under different settings, taking into account two important metrics such as network connectivity and user coverage. Simulation results demonstrate the effectiveness and merits of COA in finding optimal mesh routers locations when compared to other optimization algorithms such as Firefly algorithm (FA), Particle Swarm optimization (PSO), Whale optimization algorithm (WOA), Genetic algorithm (GA), Bat algorithm (BA), African Vulture optimization algorithm (AVOA), Aquila Optimizer (AO), Bald Eagle Search optimization (BES), Coronavirus herd immunity optimizer (CHIO), and Salp Swarm algorithm (SSA).

关键词： optimization Genetic algorithms Measurement Wireless mesh networks Heuristic algorithms Costs Topology coyote optimization algorithm mesh router nodes placement meta-heuristics network design wireless mesh networks

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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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Fuzzy Hybrid coyote optimization algorithm for Image Thresholding

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Computers, Materials & Continua 2022年第8期72卷 3073-3090页

作者： Linguo Li Xuwen Huang Shunqiang Qian Zhangfei Li Shujing Li Romany F.Mansour School of Computer Nanjing University of Posts and TelecommunicationsNanjing210003China School of Computer and Information Engineering Fuyang Normal UniversityFuyang236041China Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In order to address the problems of coyote optimization algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid coyote optimization algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is *** chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic *** sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization ***,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard *** combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then *** the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature *** on the above improvements,the coyote optimization algorithm has better global convergence and computational *** simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

关键词： coyote optimization algorithm image segmentation multilevel thresholding logistic chaotic map hybrid inverse learning strategy

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