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Study on site selection of municipal solid waste incineration plant based on swarm optimization algorithm

WASTE MANAGEMENT & RESEARCH 2022年第2期40卷 205-217页

作者： Jiang, Shan Li, Zhigang Gao, Chuang Univ Sci & Technol Liaoning Sch Elect & Informat Engn 185Qianshan Middle Rd Anshan 114051 Liaoning Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan Shandong Peoples R China

In this paper, based on an improved swarm optimization algorithm, a new site selection model of a municipal solid waste (MSW) incineration plant is proposed. First, the whale optimization algorithm and particle swarm optimization algorithm are combined according to certain rules to improve the performance of the hybrid algorithm. Through a verification of the single and multi-peak functions, the results show that the algorithm achieves a good performance. The location model of the MSW incineration plant is based on many factors, including the economy, environmental protection, population scale, and operation cost. Finally, based on a sample analysis, a new location model of an MSW incineration plant is used to select the location of an Anshan MSW incineration plant, and a reasonable location is obtained.

关键词： Garbage collection MSW incineration plant site selection model swarm optimization algorithm particle swarm optimization whale optimization algorithm

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A Class of Sequential Blind Source Separation Method in Order Using swarm optimization algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2016年第9期35卷 3220-3243页

作者： Wang Rongjie Zhan Yiju Zhou Haifeng Jimei Univ Marine Engn Inst Xiamen 361021 Peoples R China Fujian Prov Key Lab Naval Architecture & Ocean En Xiamen 361021 Peoples R China Sun Yat Sen Univ Sch Engn Guangzhou 510006 Guangdong Peoples R China

We consider the problem of sequential, blind source separation in some specific order from a mixture of sub- and sup-Gaussian sources. Three methods of separation are developed, specifically, kurtosis maximization using (a) particle swarm optimization, (b) differential evolution, and (c) artificial bee colony algorithm, all of which produce the separation in decreasing order of the absolute kurtosis based on the maximization of the kurtosis cost function. The validity of the methods was confirmed through simulation. Moreover, compared with other conventional methods, the proposed method separated the various sources with greater accuracy. Finally, we performed a real-world experiment to separate electroencephalogram (EEG) signals from a super-determined mixture with Gaussian noise. Whereas the conventional methods separate simultaneously EEG signals of interest along with noise, the result of this example shows the proposed methods recover from the outset solely those EEG signals of interest. This feature will be of benefit in many practical applications.

关键词： Blind source separation Kurtosis Sub-Gaussian distribution Sup-Gaussian distribution swarm optimization algorithm

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Design of a heat exchanger working with organic nanofluids using multi-objective particle swarm optimization algorithm and response surface method

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 119卷 922-930页

作者： Hemmat Esfe, Mohammad Mahian, Omid Hajmohammad, Mohammad Hadi Wongwises, Somchai Imam Hossein Univ Dept Mech Engn Tehran Iran King Mongkuts Univ Technol Thonburi Fac Engn Dept Mech Engn Fluid Mech Thermal Engn & Multiphase Flow Res Lab Bangkok 10140 Thailand Ferdowsi Univ Mashhad Ctr Adv Technol Mashhad Iran Acad Sci Royal Soc Thailand Sanam Suea Pa Bangkok 10300 Thailand

In this study, the Pareto optimal design of COOH-MWCNTs nanofluid was investigated to reduce pressure drop and increase the relative heat transfer coefficient. Objective function modeling was based on empirical data, the solid volume fraction, and Reynolds number and then simulated with the response surface method in Design Expert software. After the objective function approximation, the regression coefficient of more than 0.9 for this study indicated the high accuracy of modeling through the RSM. To implement the optimization process, the powerful multi-objective particle swarm optimization algorithm was used. To show the correct optimization process, the results of the first and last generations of optimization are presented at the Pareto front, with all parts of it being non-dominant and optimized. Optimal results showed that to achieve a minimum pressure drop, the relative solid volume fraction should be at the minimum interval, and to achieve the maximum heat transfer coefficient, the relative solid volume fraction should be at the maximum interval. In addition, all optimal parts have the Reynolds number in the maximum range. At last, the optimum locations are presented, and the designer can select from these -optimal points. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Organic nanofluids Heat exchanger design optimization swarm optimization algorithm Response surface method

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RETRACTED: Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and swarm optimization algorithm (Retracted article. See vol. 128, pg. 733, 2023)

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WIRELESS PERSONAL COMMUNICATIONS 2020年第3期114卷 1865-1886页

作者： Tamilarasi, K. Jawahar, A. Rajalakshmi Engn Coll Dept Informat Technol Chennai Tamil Nadu India SSN Coll Engn Dept Elect & Commun Engn Chennai Tamil Nadu India

In medical field, securing every patient's record is main concern, ascribed to many fraudulent cases occurring in the health sector. The data of every individual must be engraved and sent into end-user without any issues. Mainly in the healthcare industry, where thoughts are often focused on saving someone's life and rightly so, but securing access to interfaces and computer systems that store private data like medical records is also an essential factor to consider. Data security is a corresponding action between controlling access to information while allowing free and easy access to those who need that information. Still few problems are focused by the physician in the health sector. Patient's data should be kept securely in medical provider servers so that physicians can provide proper treatments. To ensure secure storage and access management, we propose a novel hybrid lightweight encryption using swarm optimization algorithm (HLE-SO).The proposed HLE-SO technique merge Paillier encryption and KATAN algorithm, which provides the lightweight features. Generally, the lightweight encryption algorithms are affected by the key space. We introduce the swarm optimization algorithm to optimize the key space by changing the number of iteration round. Our main goal is to encrypt the medical data (EEG signal) and send to end user by utilizing proposed HLE-SO method. Finally, the implementation is done with MATLAB tool with different EEG signal data set. The simulation results of proposed HLE-SO technique is compared with the existing state-of-art techniques in terms of different performance metrics are MSE, PSNR, SSIM, PRD, encryption time and decryption time.

关键词： Data security Encryption swarm optimization algorithm EEG Decryption

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RETRACTED: Analysis of mental health on personality and social psychology using swarm optimization algorithm (Retracted Article)

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第1期14卷 488-488页

作者： Zhang, Ting An, Kangqi Zhejiang Business Coll Hangzhou 310053 Zhejiang Peoples R China Peking Univ Sch Psychol & Cognit Sci Beijing 100871 Peoples R China

With the higher demand for economic life, human society is undergoing unprecedented changes, and the traditional outlooks on values are challenged. Since the birth of the psychoanalysis school, personality is put forward by many psychological schools, and many theories and models related to personality are derived. There are significant differences between China and the Western countries in the connotation concept and interaction with the environment. The differences are caused by the differences between Chinese and Western cultures, so it is necessary to have an in-depth theoretical discussion and empirical research on personality. In this study, a comparison between the Chinese personality model and the western personality model is conducted by using the swarm optimization algorithm to explore the development of the Chinese personality model. This provides a reference for personality shaping in the modernization. The social psychologists in China should have a close communication with foreign scholars and carry out cooperative research to accelerate the development of the personality and social psychology of Chinese people.

关键词： Personality swarm optimization algorithm Social psychology Mental health

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Gait detection of lower limb exoskeleton robot integrating visual perception and geometric features

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INTELLIGENT SERVICE ROBOTICS 2025年 1-23页

作者： Huang, Binhao Lv, Jian Qiang, Ligang Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China Guizhou Aerosp Control Technol Co Ltd Guiyang Peoples R China

Efficient extraction of gait features is essential for enhancing human-machine collaboration in wearable devices such as lower limb exoskeletons. This study combines the Yolov7-tiny target detection model with the MediaPipe framework to propose a method for extracting gait information from visual data, eliminating the dependence on sensors typical of traditional gait data extraction methods. It derives three gait feature parameter calculation methods suited to lower limb geometric characteristics and classifies gait phases based on plantar pressure distribution. A support vector machine optimized by the improved giant armadillo optimization algorithm was introduced as a machine learning classifier for performance validation. Comparisons with three other optimization algorithms validated the accuracy and superiority of the gait detection method. The impact of camera spatial positioning on detection results and the variation in detection accuracy across different subjects were also analyzed, along with anti-interference testing. Experimental results show that the proposed lower limb gait detection method achieves an accuracy of up to 99.2%, meeting the precision requirements of wearable devices. Camera distance significantly influences detection results, although angle differences showed no significant effect. Detection accuracy remained consistent across subjects, and the proposed method demonstrated some resistance to spatial interference, indicating its effectiveness in performing gait detection for exoskeletons.

关键词： Visual perception Gait detection Machine learning Exoskeleton geometric feature swarm optimization algorithm

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optimization of Dimensional Factors Using AI Technique Affecting Solar Dryer Efficiency for Drying Agricultural Materials

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Computers, Materials & Continua 2025年第4期83卷 845-860页

作者： Ravendra Kumar Ray A.C.Tiwari Department of Mechanical Engineering University Institute of Technology(UIT-RGPV)Bhopal462033India

The design and development of solar dryers are crucial in regions with abundant solar energy,such as Bhopal,India,where seasonal variations significantly impact the efficiency of drying *** paper is focused on employing a comprehensive mathematical model to predict the dryer’s performance in drying the materials such as banana *** enhance this model,Hyper Tuned swarm optimization with Gradient Tree(HT_SOGT)was utilized to accurately predict and determine the optimal size of the dryer dimensions considering various mathematical calculations for material *** predictive model considered the influence of seasonal fluctuations,ensuring an efficient drying process with an objective function to optimize the drying time of an average of 7 hrs throughout the *** all recorded ambient temperatures(ranging from 16.985○C to 31.4○C),the outlet temperature of the solar dryer is consistently higher,ranging from 39.085○C to 66.2○*** results show that the optimized dryer design,based on HT_SOGT modelling,significantly improves drying efficiency of the materials across varying conditions,making it suitable for sustainable applications in agriculture and food processing industries in the Bhopal region.

关键词： Solar dryer swarm optimization algorithm drying time drying efficiency irradiation agricultural materials

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Scheduling optimization for UAV communication coverage using virtual force-based PSO model

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Digital Communications and Networks 2024年第4期10卷 1103-1112页

作者： Jianguo Sun Wenshan Wang Sizhao Li Qingan Da Lei Chen College of Computer Science and Technology Harbin Engineering UniversityHarbinHeilongjiang150001China College of Engineering and Computing Georgia Southern UniversityStatesboroGA30458USA

When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication ***,it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy *** this paper,we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle swarm optimization(AVF-PSO)*** particular,we first introduce the gravity model into the traditional Particle swarm optimization(PSO)algorithm so as to increase the probability of full ***,the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be ***,in order to reduce the communication interference between UAVs,we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of *** results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art *** example,in the target area of any size,the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%,and 2.0–56.1%,***,the proposed scheduling algorithm is high adaptable to diverse execution environments.©2022 Published by Elsevier Ltd.

关键词： Multi-UAVs Ad hoc network Area collaborative coverage Gravity model swarm optimization algorithm Random topology

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A data-driven approach for multi-objective unit commitment under hybrid uncertainties

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ENERGY 2018年 164卷 722-733页

作者： Zhou, Min Wang, Bo Li, Tiantian Watada, Junzo Nanjing Univ Sch Management & Engn Nanjing 210093 Jiangsu Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

Recent years, renewable energy has taken growing penetration in power systems due to the energy shortage and environmental concerns. As a result, system operators encounter increasing difficulties in solving unit commitment optimization. In this paper, a data-driven unit commitment model is proposed to handle the hybrid uncertainties of wind power and future load. First, a non-parameter kernel density method is utilized to represent the above hybrid uncertainties, and a novel bandwidth selection strategy for the above method is then proposed to capture the inherent correlation between uncertainty representation and unit commitment. Second, a Monte Carlo simulation is developed to integrate the hybrid uncertainties into Value-at-Risk to get a comprehensive system reliability measurement. Third, considering that system operators might be interested in the inherent conflict between reliability and economy, minimizing operation costs and maximizing system reliability are taken as two objectives in the model. To get more practical schedules, the transmission line constraint is considered as well when building the mathematical model. Additionally, by integrating the reinforcement learning mechanism, a novel multi objective particle swarm optimization algorithm is proposed to solve the complicated nonlinear model. Finally, several experiments were performed to demonstrate the effectiveness of this research. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-objective unit commitment Non-parameter kernel density method Hybrid uncertainties Reinforcement learning-based particle swarm optimization algorithm

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Multi-objective optimal scheduling of microgrid with electric vehicles

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ENERGY REPORTS 2022年 8卷 4512-4524页

作者： Mei, Yu Li, Bin Wang, Honglei Wang, Xiaolin Negnevitsky, Michael Guizhou Univ Elect Engn Coll Guiyang 550025 Peoples R China Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Peoples R China Key Lab Internet Collaborat Intelligent Mfg Guizh Guiyang 550025 Guizhou Peoples R China Univ Tasmania Sch Engn Hobart Tas 7005 Australia

With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid. (c) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Microgrid Electric vehicles Multi-objective optimization Two-person zero-sum game Adaptive simulated annealing particle swarm optimization algorithm

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