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The hybrid method based on ant colony optimization algorithm in multiple factor analysis of the environmental impact of solar cell technologies

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第6期17卷 6342-6354页

作者： Dong, Bo Luzin, Alexey Gura, Dmitry Liaoning Univ Sch Innovat & Entrepreneurship Chongshan Rd 66 Shenyang 110000 Liaoning Peoples R China Sechenov First Moscow State Med Univ Dept Chem Trubetskaya St 8 Moscow 119991 Russia Kuban State Technol Univ Dept Cadastre & Geoengn Moscow Str2 Bldg B Krasnodar 350072 Russia Kuban State Agr Univ Dept Geodesy 13 Kalinina Str Krasnodar 350044 Russia

The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particle swarm and ant colony optimization algorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25-30 years mostly last 20-25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.

关键词： Ant Colony optimization algorithm new technologies particle swarm optimization algorithm PV panel waste solar energy

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Design of Estimation algorithm of Island Intelligent Tourist Volume Based on Data Mining

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JOURNAL OF COASTAL RESEARCH 2020年第Sup1期000卷 985-990页

作者： Zhang, Xia Wang, Bin Yulin Univ Sch Management Yulin 719000 Peoples R China Yulin Univ Student Off Yulin 719000 Peoples R China

The traditional intelligent neural network PID control method is not conducive to the estimation of the quantity control of the island intelligent tourism. In this paper, a particle swarm optimization (PSO) algorithm is proposed to estimate the tourist volume of island intelligent tourism. The fuzzy PID control method is used to establish the intelligent output scheduling control model. In this paper, the adaptive scheduling weighting coefficient of island tourism is introduced, and the radial optimization of iterative steps of particle swarm optimization is used to estimate the island intelligence tourism. On this basis, a mathematical model of intelligent tourism evaluation based on data mining theory is established. The simulation results show that the algorithm has high precision and good convergence, and improves the robustness of island tourism scheduling control.

关键词： Intelligent tourism particle swarm optimization algorithm data mining predictive control

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Cultural particle swarm optimization algorithm and Its Application

Cultural Particle Swarm Optimization Algorithm and Its Appli...

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24th Chinese Control and Decision Conference (CCDC)

作者： Zhou Wei Bu Yan-ping E China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9781457720727

Based on cultural algorithm and classical particle swarm optimization (PSO) algorithm, a cultural particle swarm optimization (CPSO) algorithm is proposed. In the improved algorithm, double evolutionary mechanisms are used. The population space and the belief space of cultural algorithm are redesigned. The proposed model was used to solve the partner selection problem of virtual enterprise. In a virtual enterprise, the whole task can be accomplished by the cooperation among those candidate partners. The optimal objective is to minimize the total cost and completing time. Finally, the performance of the algorithm is evaluated by simulations. Results demonstrate the feasibility and efficiency of the proposed algorithm.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

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MECHANICS & INDUSTRY 2021年第1期22卷

作者： Kang, Jianxiong Lu, Yanjun Luo, Hongbo Li, Jie Hou, Yutao Zhang, Yongfang Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Sch Printing Packaging Engn & Digital Media Techn Xian 710048 Peoples R China

The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

关键词： Wear assessment cylinder liner support vector regression fuzzy uncertainty particle swarm optimization algorithm

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Task Scheduling Model of Edge Computing for AI Flow Computing in Internet of Things

Task Scheduling Model of Edge Computing for AI Flow Computin...

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作者： Luanqi Liu Haifeng Wang Yinzhe Liu Ming Zhang School of information science and engineering Linyi University

With the development of intelligent applications of the Internet of things,the sharp increase of data scale and the improvement of computational model complexity in the edge computing environment put forward higher requirements for the performance of edge *** optimizing the scheduling of AI data intensive computing tasks can greatly improve the overall performance of edge ***,a novel particle swarm optimization algorithm is proposed to realize task scheduling in edge computing environment by calculating task scheduling to optimize task execution time and scheduling *** the speed formula of particle swarm optimization algorithm to improve the convergence speed and row performance of the ***,on the Cloudsim simulation platform,the proposed particle group algorithm LK-PSO was compared with the other four benchmark *** results showed that the method task scheduling cost and execution time were effectively *** method can effectively improve the resource utilization rate of marginal computing and improve the efficiency of marginal computing power.

关键词： Internet of things Edge computing Task scheduling AI data-intensive computing tasks particle swarm optimization algorithm

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Application of Option Pricing Theory in Water Conservancy Finance

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JOURNAL OF COASTAL RESEARCH 2020年第Spe104期36卷 633-636页

作者： Lu, Xiaoli He, Guang Chongqing Technol & Business Univ Res Ctr Econ Upper Reaches Yangtze River Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Coll Math & Stat Chongqing 400067 Peoples R China

Financial support for water conservancy construction is an important approach to promote the development of water conservancy economy. In order to deal with numerical solution for stock option in water conservancy finance, a hybrid optimization algorithm is proposed. By virtue of the relation between Black-Scholes model and heat equation, a class of heat equations with initial-boundary values is established based on Schwarz waveform relaxation algorithm, meanwhile particle swarm optimization algorithm is applied to estimate parameters in option pricing model. In numerical experiments, the hybrid optimization algorithm is used to seek the approximate value of call option based on water concept stock, and it obtains better estimation results than existed methods.

关键词： Black-Scholes model water concept stock schwarz waveform relaxation algorithm heat equation particle swarm optimization algorithm

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

The State of Charge Estimation of Lithium-ion Batteries Usin...

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第34届中国控制与决策会议

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

Accurate state of charge(SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or ***,it is difficult to get an accurate value of SOC since it is an inner state of a battery cell,which cannot be directly *** order to improve the estimation accuracy of SOC,this paper develops a SOC estimation model for a lithium-ion battery using a particle swarm optimization-Extreme Learning Machine(PSO-ELM) *** PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM *** inputs to the PSO-ELM model are the battery voltage,current,and temperature,and the output is the actual SOC *** performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error(MSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and SOC *** results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge particle swarm optimization algorithm Extreme Learning Machine

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Integration of particle swarm optimization and genetic algorithm for dynamic clustering

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INFORMATION SCIENCES 2012年 195卷 124-140页

作者： Kuo, R. J. Syu, Y. J. Chen, Zhen-Yao Tien, F. C. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Vanguard Int Semicond Corp Hsinchu Taiwan De Lin Inst Technol Dept Business Adm New Taipei City Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei Taiwan

Although the algorithms for cluster analysis are continually improving, most clustering algorithms still need to set the number of clusters. Thus, this study proposes a novel dynamic clustering approach based on particle swarm optimization (PSO) and genetic algorithm (GA) (DCPG) algorithm. The proposed DCPG algorithm can automatically cluster data by examining the data without a pre-specified number of clusters. The computational results of four benchmark data sets indicate that the DCPG algorithm has better validity and stability than the dynamic clustering approach based on binary-PSO (DCPSO) and the dynamic clustering approach based on GA (DCGA) algorithms. Furthermore, the DCPG algorithm is applied to cluster the bills of material (BOM) for the Advantech Company in Taiwan. The clustering results can be used to categorize products which share the same materials into clusters. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Cluster analysis Dynamic clustering particle swarm optimization algorithm Genetic algorithm

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Thermo-economic optimization of the direct-condensation radiant heating panel: A numerical-based approach

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ENERGY AND BUILDINGS 2021年 240卷 110908-110908页

作者： Shao, Suola Zhang, Huan Liu, Zhikai You, Shijun Wang, Yaran Tianjin Univ Sch Environm Sci & Engn Haihe Educ Area Tianjin 300350 Peoples R China Tianjin Univ Ey Lab Efficient Utilizat Low & Medium Grade Ener Minist Educ China Tianjin 300350 Peoples R China

The direct-condensation radiant heating panel (DRHP) is considered as an efficient heating terminal in space heating. In this study, a numerical-based optimization approach is proposed for the thermoeconomic performance improvement of the DRHP. An improved numerical model of the DRHP considering the heat conduction of the composite straight-and-circular fins and that of the connecting segments is established. The analytical solutions of the heat conduction of the composite straight-and-circular fins are derived to improve the prediction accuracy and computation speed. The model is validated by the experimental data. Based on the proposed model, the particle swarm optimization (PSO) algorithm is adopted to maximize the heating capacity under per unit cost of the DRHP. The optimization constraints are determined with the parametric analysis and the iterations are examined to be 30. Based on the optimization approach, the optimized DRHPs are obtained for heat pump units with different output powers, and the heating capacity under per unit cost of the optimized DRHP is increased by 44.2%. The proposed optimization approach is appropriate for the optimization of DRHP. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Direct-condensation radiant heating panel Improved mathematical model Parametric analysis particle swarm optimization algorithm Thermo-economic optimization

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Expeditious COVID-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operators

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APPLIED SOFT COMPUTING 2021年 104卷 107197-107197页

作者： Issa, Mohamed Zagazig Univ Fac Engn Comp & Syst Dept Zagazig Egypt Nahda Univ Fac Comp & Informat Bani Suwayf Egypt

COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences. The huge time consumption of the standard global alignment algorithm is its main limitation especially for sequences with huge lengths. This work proposed a fast global alignment tool (G-Aligner) based on meta-heuristic algorithms that estimate similarity measurements near the exact ones at a reasonable time with low cost. The huge length of sequences leads G-Aligner based on standard Sine-Cosine optimization algorithm (SCA) to trap in local minima. Therefore, an improved version of SCA was presented in this work that is based on integration with PSO. Besides, mutation and opposition operators are applied to enhance the exploration capability and avoiding trapping in local minima. The performance of the improved SCA algorithm (SP-MO) was evaluated on a set of IEEE CEC functions. Besides, G-Aligner based on the SP-MO algorithm was tested to measure the similarity of real biological sequence. It was used also to measure the similarity of the COVID-19 virus with the other 13 viruses to validate its performance. The tests concluded that the SP-MO algorithm has superiority over the relevant studies in the literature and produce the highest average similarity measurements 75% of the exact one. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 Bioinformatics Pairwise global alignment Sine-Cosine optimization algorithm particle swarm optimization algorithm

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