Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to opt...

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Vydané v:	Journal of bionics engineering Ročník 21; číslo 5; s. 2540 - 2568
Hlavní autori:	Hu, Gang, Wang, Sa, Houssein, Essam H.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Nature Singapore 01.09.2024 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Combinatorial analysis Electricity Electricity consumption Engineering Evolutionary computation Optimization models Parameters Power consumption Predictions Research Article Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping
ISSN:	1672-6529, 2543-2141
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Abstract	The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1, r , ξ , Csz ) to realize the simulation and prediction of China’s daily electricity consumption. Firstly, a novel MCOA is proposed in this paper, by making the following improvements to the Coati Optimization Algorithm (COA): (i) Introduce improved circle chaotic mapping strategy. (ii) Fusing Aquila Optimizer, to enhance MCOA's exploration capabilities. (iii) Adopt an adaptive optimal neighborhood jitter learning strategy. Effectively improve MCOA escape from local optimal solutions. (iv) Incorporating Differential Evolution to enhance the diversity of the population. Secondly, the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm, the improved optimization algorithm, and the hybrid algorithm on the CEC2019 and CEC2020 test sets. Finally, in this paper, MCOA is used to optimize the parameters of TDGM(1,1, r , ξ , Csz ), and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models, including seven intelligent algorithm-optimized TDGM(1,1, r , ξ , Csz ), and seven forecasting models. The experimental results show that the error of the proposed method is minimized, which verifies the validity of the proposed method.
AbstractList	The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz) to realize the simulation and prediction of China’s daily electricity consumption. Firstly, a novel MCOA is proposed in this paper, by making the following improvements to the Coati Optimization Algorithm (COA): (i) Introduce improved circle chaotic mapping strategy. (ii) Fusing Aquila Optimizer, to enhance MCOA's exploration capabilities. (iii) Adopt an adaptive optimal neighborhood jitter learning strategy. Effectively improve MCOA escape from local optimal solutions. (iv) Incorporating Differential Evolution to enhance the diversity of the population. Secondly, the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm, the improved optimization algorithm, and the hybrid algorithm on the CEC2019 and CEC2020 test sets. Finally, in this paper, MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz), and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models, including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz), and seven forecasting models. The experimental results show that the error of the proposed method is minimized, which verifies the validity of the proposed method. The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1, r , ξ , Csz ) to realize the simulation and prediction of China’s daily electricity consumption. Firstly, a novel MCOA is proposed in this paper, by making the following improvements to the Coati Optimization Algorithm (COA): (i) Introduce improved circle chaotic mapping strategy. (ii) Fusing Aquila Optimizer, to enhance MCOA's exploration capabilities. (iii) Adopt an adaptive optimal neighborhood jitter learning strategy. Effectively improve MCOA escape from local optimal solutions. (iv) Incorporating Differential Evolution to enhance the diversity of the population. Secondly, the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm, the improved optimization algorithm, and the hybrid algorithm on the CEC2019 and CEC2020 test sets. Finally, in this paper, MCOA is used to optimize the parameters of TDGM(1,1, r , ξ , Csz ), and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models, including seven intelligent algorithm-optimized TDGM(1,1, r , ξ , Csz ), and seven forecasting models. The experimental results show that the error of the proposed method is minimized, which verifies the validity of the proposed method.
Author	Houssein, Essam H. Wang, Sa Hu, Gang
Author_xml	– sequence: 1 givenname: Gang orcidid: 0000-0003-2274-7482 surname: Hu fullname: Hu, Gang email: hg_xaut@xaut.edu.cn organization: Department of Applied Mathematics, Xi’an University of Technology, School of Computer Science and Engineering, Xi’an University of Technology – sequence: 2 givenname: Sa surname: Wang fullname: Wang, Sa organization: Department of Applied Mathematics, Xi’an University of Technology – sequence: 3 givenname: Essam H. surname: Houssein fullname: Houssein, Essam H. organization: Faculty of Computers and Information, Minia University
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SubjectTerms	Algorithms Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Combinatorial analysis Electricity Electricity consumption Engineering Evolutionary computation Optimization models Parameters Power consumption Predictions Research Article
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Title	Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China
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