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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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RETRACTED: Analysis of mental health on personality and social psychology using swarm optimization algorithm (Retracted Article)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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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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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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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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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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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 of twin automatic stacking cranes and automated guided vehicle considering buffer mode on automated container terminal

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Zhu, Jin Ma, Feiyang Shanghai Maritime Univ Inst Logist Sci & Engn 1550 Haigang Ave Shanghai Peoples R China

To improve the efficiency of automatic stacking crane (ASC) and automated guided vehicle (AGV) scheduling in synchronized import and export mode at automated container terminal (ACT), with the objective of minimizing the maximum ASC completion time and AGV wait time, a twin ASCs and AGV scheduling model is established, which considers both the size of handshake area and buffer, and the location constraints of handshake area. Longest transport time priority (LTTP) and traversal priority (TP) heuristics strategies are designed to adjust the execution order of ASCs to reduce the wait time of ASCs. Based on the comprehensive learning particle swarm optimization algorithm (CLPSO), the discrete coding CLPSO algorithm (CLDPSO) is developed by designed permutation coding for ASC execution order and integer coding for AGV numbering, and the convergence speed is improved by dynamically adjusting the weight parameters. The experimental results show that increasing the handshake area and buffer size can reduce the fitness value by 3.64 % and 5.58 % on average respectively, the selected optimal handshake area location can reduce the fitness value by 3.51 % on average compared with the conventional location, the TP strategy can reduce the waiting time of ASC by up to 16.89 %. Compared to the PSO and DPSO algorithms, the fitness value of CLDPSO can reduce the fitness value by 13.83 % and 7.80 %, and the computation time is reduced by 10.67 % and 7.43 % on average, respectively.

关键词： Scheduling Automated guided vehicles Automated stacking cranes Heuristics strategy Comprehensive learning discrete particle swarm optimization algorithm

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Brain storm optimization algorithm for solving knowledge spillover problems

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12247-12260页

作者： Cheng, Shi Zhang, Mingming Ma, Lianbo Lu, Hui Wang, Rui Shi, Yuhui Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Northeastern Univ Coll Software Shenyang 110819 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particularly, in a knowledge spillover problem (KSP), cooperation strategies among more than hundreds of individuals need to be simulated. At the same time, the brain storm optimization (BSO) algorithm, which is a data-driven and model-driven hybrid paradigm, has the potential to simulate the complex behaviors in a group of simple individuals. In this paper, a modified BSO algorithm has been used to solve KSP from the perspective of evolutionary game theory. Knowledge spillover (KS) is the sharing or exchanging of knowledge resources among individuals. Firstly, the KS and evolutionary game theory were introduced. Then, the KS model and KS optimization problems were built from the evolutionary game perspective. Lastly, the modified BSO algorithms were utilized to solve KS optimization problems. Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. More efficient algorithms could be designed to solve different real-world evolutionary game problems.

关键词： Brain storm optimization Evolutionary game theory Knowledge spillover model swarm optimization algorithm

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