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Modeling and optimization approach for phytoremediation of formaldehyde from polluted indoor air by Nephrolepis obliterata plant

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第8期30卷 21345-21359页

作者： Teiri, Hakimeh Dehghani, Mansooreh Mohammadi, Farzaneh Samaei, Mohammad Reza Hajizadeh, Yaghoub Pourzamani, Hamidreza Rostami, Saeid Shiraz Univ Med Sci Sch Hlth Dept Environm Hlth Engn Student Res Comm Shiraz Iran Shiraz Univ Med Sci Sch Hlth Dept Environm Hlth Engn Shiraz Iran Isfahan Univ Med Sci Fac Hlth Esfahan Iran Isfahan Univ Med Sci Res Inst Primordial Prevent Noncommunicable Dis Esfahan Iran

This study aimed to model the removal of formaldehyde as an indoor air pollutant by Nephrolepis obliterata (***.) ***. plant using response surface methodology (RSM) and artificial neural network (ANN) models, and optimization of the models by particle swarm optimization algorithm (pso). The data obtained in pilot-scale experiments under a controlled environment were used in this study. The effects of parameters on the removal efficiency such as formaldehyde concentration, relative humidity, light intensity, and leaf surface area were empirically investigated and considered as model parameters. The results of the RSM model, with power transformation, were in meaningful compromise with the experiments. A multilayer perceptron (MLP) neural network was also designed, and the mean of squared error (MSE), mean absolute error (MAE), and R-2 were used to evaluate the network. Several training algorithms were assessed and the best one, the Levenberg Marquardt (LM), was selected. The pso algorithm proved that the highest removal efficiency of formaldehyde was obtained in the presence of light, maximum leaf surface area and relative humidity, and at the lowest inlet concentration. The empirical system breakthrough occurred at 15 mg/m(3) of formaldehyde, and the maximum elimination capacity was about 0.96 mg per m(2) of leaves. The findings indicated that the ANN model predicted the removal efficiency more accurately compared to the RSM model.

关键词： ANN model Formaldehyde Indoor air pollution Phytoremediation pso algorithm RSM model

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Solving redundant inverse kinematics of CMOR based on chaos-driven particle swarm optimization algorithm

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FUSION ENGINEERING AND DESIGN 2023年第1期192卷

作者： Zhao, Fang Cheng, Yong Pan, Hongtao Cheng, Yang Zhang, Xi Wu, Bo Hu, Youmin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China Chinese Acad Sci Inst Plasma Phys Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

The China Fusion Engineering Test Reactor (CFETR) multipurpose overload robot (CMOR) is used to maintain the vacuum chamber inner components damaged by heat loads, electromagnetic fields, and nuclear radiation. The CMOR is a redundant robot whose configuration does not meet the condition of the Pieper criterion. Redundant kinematic problems can only be solved by numerical methods, not analytical methods. Conventional numerical iterative methods include considerable computational load, accumulated errors, and the Jacobian matrix's singularity. To solve redundant inverse kinematics efficiently, we compared the behaviors of 25 versions of particle swarm optimization (pso) algorithms with 12 one-dimensional chaotic maps under unimodal and multimodal test functions. Moreover, we selected three chaos-driven pso algorithms with optimal convergence performance to address CMOR inverse kinematics. The experimental results indicated that chaos-driven pso algorithms have higher computational efficiency and can effectively improve the speed and accuracy of algo-rithm convergence. This proposed algorithm delivers a novel and efficient method for inverse kinematics of redundant robots based on chaotic maps.

关键词： CMOR pso algorithm chaos -driven inverse kinematics

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A three-stage pso-based methodology for tuning an optimal PD-controller for robotic arm manipulators

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 381-396页

作者： Tutunji, Tarek A. Al-Khawaldeh, Mustafa Alkayyali, Malek Philadelphia Univ Mechatron Engn Dept Amman Jordan Clemson Univ Dept Mech Engn Clemson SC USA

Six degrees-of-freedom (DoF) robot manipulators are dynamically coupled and highly nonlinear multi-variable systems. Calculating the optimal joint angles from the operational space (i.e. inverse kinematics) and designing the optimal joint controller parameters are two important research topics. This paper proposes a 3-stage Particle Swarm Optimization (pso)-based methodology for solving the inverse kinematics and optimizing the controller parameters. In the first stage, a pso algorithm solves the inverse kinematics problem by minimizing a multi-objective cost function in the operational space (i.e. the error in the end-effector's pose) and therefore finds the optimal joint angles. In the second stage, polynomial functions generate the desired trajectory between the initial and final poses in the joint space. Finally, a second pso algorithm tunes six proportional-derivative (PD) controllers, one for each joint, to track the desired trajectory by minimizing another multi-objective cost function in the joint space. Two case studies, based on six DoF Puma 560, validate the performance of the proposed methodology. Simulation results show that the proposed 3-stage methodology provides fast and accurate results as the pso algorithms are effective in solving the inverse kinematics problem and tuning the optimal PD parameters.

关键词： Robot manipulator Nonlinear dynamics Optimization pso algorithm Inverse kinematics Trajectory tracking PD-control PUMA 560

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Using pso and ABC Routing algorithms Reducing Consumed Energy in Underwater Wireless Sensor Networks

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2022年第2期46卷 367-380页

作者： Kharati, Ehsan Islamic Azad Univ Dept Comp Engn Arak Branch Arak Iran

The main challenge in Underwater Wireless Sensor Networks (UWSN) is reducing consumed energy and while extending the lifetime of the network. Mobile Sinks (MSs) and optimum data transmission routing between Sensor Nodes (SNs) are widely employed to overcome this problem. Particle Swarm Optimization (pso) is one of the heuristic algorithms for optimizing data routing, but it has some drawbacks, including the risk of early convergence, inaccurate solution presentation, and local minimal. One of the reasons for this issue is a lack of thorough searching and checking of the search space. To tackle this problem, in this paper we propose a routing algorithm based on pso and Artificial Bee Colony (ABC) algorithms to investigate the search space more accurately, lower the chance of falling in local minimal, and prevent wrong solution and early convergence. However, combining the two pso and ABC algorithms slows the suggested algorithm, which can be solved by executing parallel loops. Simulation results show that increasing the number of iterations and SNs improve the routing performance in the proposed algorithm better than the pso, Genetic algorithm, and Clustering algorithms. In addition, the network lifetime will be increased by about 5 to 10%, consumed energy will be decreased by about 5 to 20%, and except for the start period, the number of disabled SNs will be reduced by 20 to 30%;nevertheless, the run time will be increased.

关键词： pso algorithm ABC algorithm Underwater Wireless Sensor Networks Data Routing

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Thermal circuit model parameters identification of oil-immersed transformer based on pso algorithm

Thermal circuit model parameters identification of oil-immer...

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2015 4th International Conference on Mechatronics,Materials,Chemistry and Computer Engineering(ICMMCCE 2015)

作者： Linli Zhang Lisheng Li Bin Jiang Rong Li Hongmei Li Xiao Dong State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power Company

ISBN: (纸本)9781510817371

The thermal circuit model of oil-immersed transformer needs to improve its accuracy in predicting the winding hottest-spot temperature, especially in the case of overload and different cooling modes. In the case of overload condition, the estimate values obtained from most existing models are smaller than the actual measurement values, which increase the potential of transformer overheating fault because of the estimate shortage. These limitations are mainly due to the thermal circuit parameters which are deviating when overloaded or having different cooling modes. In order to overcome these limitations, a parameter identification and correction approach based on pso algorithm is proposed. The approach works on daily load pattern, permits better accuracy and improves the estimation security margin of thermal circuit in the presence of overload condition and different cooling modes.

关键词： power transformer hot spot thermal circuit model pso algorithm parameter identification

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A self-adaptive harmony pso search algorithm and its performance analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第21期42卷 7436-7455页

作者： Zhao, Fuqing Liu, Yang Zhang, Chuck Wang, Junbiao Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech Xian 710072 Peoples R China

Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHpsoS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the pso algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHpsoS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHpsoS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHpsoS outperforms original HS and the other related algorithms in terms of the solution quality and the stability. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Harmony Search algorithm pso algorithm Self-adaptive scheme Mutation strategy Markov model

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Optimizing the wells location using the Metaheuristic algorithms, as well as optimizing the drilling time of production and injection wells in one of the reservoirs in south west of Iran

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EARTH SCIENCE INFORMATICS 2024年第2期17卷 1393-1410页

作者： Nikravesh, Hamed Ranjbar, Ali Azin, Reza Persian Gulf Univ Fac Petr Gas & Petrochem Engn Petr Engn Dept Bushehr Iran

In this study, the optimization of the location of oil production and injection wells, the number of production and injection wells, and the economic evaluation in the water injection process has been carried out using three algorithms: pso, GA, and HGApso. Optimizing the oil extraction process by optimizing well locations, the optimal number of production and injection wells, and economic evaluation can improve the performance and efficiency of the process. In this study, the optimization of the water injection process into oil reservoirs using the pso, GA, and HGApso algorithms has been addressed. Additionally, economic evaluation, considering costs and profits from oil production using the best-optimized parameters, has been conducted, and the best Net Present Value (NPV) has been calculated. Initially, the optimal well location problem is modeled, taking into account reservoir characteristics and geological conditions. Using the pso and GA algorithms, the best well location and number of water injection wells have been obtained, leading to improved efficiency and cost reduction. Then, the GA algorithm is used to combine and modify viewpoints and optimize the number and location of wells. This algorithm explores optimal solutions in the well location space. Economic evaluation is performed using the best-optimized parameters by the algorithms. Costs of water injection, oil production costs, and profits from increased oil production are considered. By calculating the best Net Present Value (NPV), optimal decisions regarding the water injection process and enhancing the performance of oil reservoirs are made. Simulation results indicate that the GA algorithm outperforms other algorithms and can be used as a robust and effective method for optimizing well locations and the number of production and injection wells in the oil injection process. Ultimately, this study assists the oil industry in making better decisions about optimizing water injection and improving the

关键词： pso algorithm GA algorithm HGA pso algorithm Well location optimization

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Research and Realization of Building the Three-dimensional Runway of Unity 3D Game Based on pso algorithm

Research and Realization of Building the Three-dimensional R...

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International Conference on Computer Science and Network Technology

作者： Linghong Chen Xiaojun Huang Department of information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781467381741

A new solution for the construction of 3D Runway based on pso algorithm and Unity 3D game engine is presented in this paper. Compared with the traditional runway construction, this solution can build rich scene interface with less scene resources by the application of pso algorithm and the construction of Unity3D engine. Experiment has proved that the scheme can generate the scene reasonably which has highly entertainment value. It can realize the random and regular patterns of the scene. The scheme has proved that it can optimize the scene loading process and improve the users' experience.

关键词： Unity3D pso algorithm Three-dimensional runway parametric subharmonic oscillator RUNWAY Hunting Game theory Game animals

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pso-BP Neural Network Based Prediction Study for Nonlinear Problems

PSO-BP Neural Network Based Prediction Study for Nonlinear P...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Nan, Jiedong Medical College of Tianjin University Tianjin China

ISBN: (纸本)9798350309614

The Back Propagation (BP) neural network is commonly used for predicting nonlinear problems. However, this model is known to converge slowly and is prone to local optimisations. To address this issue, using a population intelligence optimisation algorithm to optimise the structure of the BP neural network is highly significant. This paper aims to explore the prediction of nonlinear problems by establishing the prediction model according to the three-layer architecture mode of BP neural network and using the characteristics of global optimisation of the particle swarm algorithm (particle swarm optimization, pso) to optimise the initial weights and thresholds of BP neural network, so as to propose a kind of prediction model of nonlinear problems based on pso-BP neural network. Based on the experimental results, the coefficient of determination R2 of the pso-BP neural network is 29.41% higher than that of the single BP neural network. Moreover, the RMSE is reduced by 64.08%. These findings demonstrate that the pso algorithm-optimised BP neural network has superior prediction accuracy and stability for nonlinear problems than that of the single BP neural network. This provides constructive optimisation of the structure of the BP neural network. © 2023 IEEE.

关键词： BP neural network nonlinear prediction problem particle swarm prediction model pso algorithm

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Lyapunov-based adaptive PID controller design for buck converter

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SOFT COMPUTING 2023年第9期27卷 5741-5750页

作者： Ghamari, Seyyed Morteza Khavari, Fatemeh Mollaee, Hasan Shahrood Univ Technol Fac Elect Engn Shahrood Iran

In this paper, a Lyapunov-based model reference proportional-integrated-derivative (PID) is designed using this approach for a DC / DC Buck converter. However, under a wider range of disturbances, the PID strategy is not suitable for practical applications, and the parameters must be tuned again for more reliable operations. To this end, a Lyapunov definition-based adaptive mechanism is adopted for the PID strategy, which can increase the stability and robustness of this scheme under various disturbances. Moreover, the system is considered a black-box without the need for accurate mathematical modeling of the system that can result in a lower computational burden as well as ease of implementation. The Lyapunov concept is a modern adaptive algorithm that can find optimal answers in shorter periods with more accuracy and reliable stability insurance. To examine the strength of this method, conventional fractional-order PID (FOPID) and PID control techniques are also tested to be compared with this work using the pso algorithm to tune their parameters. Finally, the results related to both simulations and experimental outputs are tested, showing faster dynamics and significant robustness of Lyapunov-based PID strategy in different challenging scenarios.

关键词： Buck converter Lyapunov stability PID controller pso algorithm Noise

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