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A Novel Dynamic Clustering Method by Integrating Marine Predators algorithm and particle swarm optimization algorithm

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IEEE ACCESS 2021年 9卷 3557-3569页

作者： Wang, N. Wang, J. S. Zhu, L. F. Wang, H. Y. Wang, G. Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.

关键词： Clustering algorithms Indexes Heuristic algorithms optimization particle swarm optimization Encoding Clustering methods Dynamic clustering marine predator algorithm particle swarm optimization algorithm

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Hybrid particle swarm optimization algorithm for text feature selection problems

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NEURAL COMPUTING & APPLICATIONS 2024年第13期36卷 7471-7489页

作者： Nachaoui, Mourad Lakouam, Issam Hafidi, Imad Natl Sch Appl Sci ENSA Khouribga 25000 Morocco Univ Sultan Moulay Slimane Fac Sci & Tech Equipe Math & Interact Beni Mellal Morocco

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set.

关键词： Text feature selection particle swarm optimization algorithm Genetic algorithm Constriction factor Chaotic map K-mean text clustering algorithm

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Research on the coupling characteristics of energy power density of cutting vibration and cutting heat based on the particle swarm optimization algorithm

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2022年第3期46卷 614-625页

作者： Li, Songyuan Li, Shuncai Li, Yuqing Vladimirovich, Petrov Anton Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Jiangsu Normal Univ JSNU SPBPU Inst Engn Xuzhou 221116 Jiangsu Peoples R China Petersburg Polytech Univ Inst Adv Mfg Technol St Petersburg 1952151 Russia

Cutting heat and cutting vibration are important basic research topics in the field of machining. Many factors affect cutting heat and cutting vibration, and cutting heat and cutting vibration also affect each other. This papermainly studied the coupling characteristics between cutting vibration and cutting heat from the perspective of energy power density. A measurement system was built to collect the time-domain signals of cutting temperature and three-dimensional cutting vibration. Through Stefan-Boltzmann's law, the cutting thermal power density represented by the cutting temperature was obtained. Frequency domain analysis dealing with the self-power spectrum density was carried out on the three-dimensional vibration acceleration, and the operation of reducing the vibration dimension was carried out by principal component analysis. Based on the particle swarm optimization algorithm, two couplingmodels between cutting heat and cutting vibrationwere established. The research showed that the coupling correlation coefficient between cutting heat and cutting vibration was above 0.6. The coupling characteristics of cutting heat and cutting vibration were strong, and the impact of cutting vibration on cutting heat was more significant. The conclusions provide theoretical guidance for studying the coupling characteristics of cutting heat and cutting vibration from the energy perspective.

关键词： cutting heat cutting vibration coupling characteristics cutting energy power density particle swarm optimization algorithm

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Finding a good shape parameter of RBF to solve PDEs based on the particle swarm optimization algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 3641-3652页

作者： Koupaei, Javad Alikhani Firouznia, Marjan Hosseini, Seyed Mohammad Mahdi Payame Noor Univ Dept Math Box 19395-3697 Tehran Iran Univ Khansar Dept Comp Engn Khansar Iran Shahid Bahonar Univ Kerman Dept Appl Math Kerman Iran

The present study aims at integrating the particle swarm optimization (PSO) algorithm with Kansa's method based on meshless collocation methods in order to determine a good shape parameter of Radial Basis Function (RBF) for solving partial differential equations (PDEs). For this purpose, we use a two-staged experimental design. While in the first stage, PSO algorithm was used to determine an optimal shape parameter for the related RBFs, in the second stage, we employed Kansa's method to estimate the RMS error for specifying approximate solutions. To study the performance of the proposed algorithm, we offer numerical results for two examples of partial differential equations and show the effectiveness of the proposed method. Numerical results demonstrated the performance superiority of the new algorithm model. The findings also indicated that the evolutionary algorithm model is more effective than the golden section search algorithm in finding a good shape parameter of RBF. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： particle swarm optimization algorithm RBF Kansa's method PDEs Evolutionary algorithm

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Estimating soot volume fraction and temperature in flames using stochastic particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2011年第1-3期54卷 217-224页

作者： Sun, Yi-Peng Lou, Chun Zhou, Huai-Chun Huazhong Univ Sci & Technol State Key Lab Coal Combust Wuhan 430074 Hubei Peoples R China

A simulation investigation for simultaneous reconstruction of distributions of temperature and soot volume fraction from multi-wavelength emission in a sooting flame using the stochastic particle swarm optimizer (PSO) algorithm is presented. The self-absorption of the flame is considered. The selection of parameters of the stochastic PSO algorithm and detection wavelengths is analyzed. The effects of measurement errors and optical thickness of the flame on the accuracy of the reconstruction are investigated. It proved that the stochastic PSO algorithm is robust and can obtain accurate distributions of temperature and soot volume fraction from line-of-sight intensities in only several wavelengths, especially in the flame with large optical thickness, while other methods neglecting self-attenuation of the flame will take some errors. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Inverse radiation problem particle swarm optimization algorithm Flame emission spectrum Soot volume fraction Temperature reconstruction

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Low Carbon optimization Scheduling of Micro Grid Based on Improved particle swarm optimization algorithm

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IEEE ACCESS 2024年 12卷 76432-76441页

作者： Sang, Yingjun Zhang, Wenzhi Ma, Jing Chen, Quanyu Tao, Jinglei Fan, Yuanyuan Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Math & Phys Huaian 223003 Peoples R China

This article proposes a low-carbon operation analysis method for micro grids based on improved particle swarm optimization algorithm. Corresponding improvements have been made to the inertia weight, learning factor, and individual extreme of the algorithm, depicting the comprehensive low-carbon operation information of micro grids under the influence of carbon emission quotas and carbon trading mechanisms from the perspective of data visualization. The low-carbon scheduling of micro grids is carried out from three perspectives: environmental protection, economy, and comprehensiveness, which compensates for the limitations of focusing on traditional low-carbon operation and provides a powerful tool for analyzing low-carbon operation of micro grids. Firstly, establish the energy consumption cost and carbon emission cost functions of the micro grid system, add the two cost functions together and take the minimum sum to form the objective function of this article. Then, based on the characteristics of each unit, a low-carbon model is constructed to constrain the carbon emissions of each unit. Finally, simulation analysis was conducted on the micro grid system based on the improved particle swarm optimization algorithm, verifying the effectiveness and practicality of the proposed algorithm. The simulation results show that the improved particle swarm optimization algorithm can quickly and effectively reduce energy consumption and carbon emission costs, and improve the comprehensive efficiency of micro grid systems.

关键词： Costs Carbon dioxide Turbines Photovoltaic systems State of charge Generators Wind power generation Microgrids particle swarm optimization Carbon emissions Emissions trading Micro grid low carbon particle swarm optimization algorithm carbon emissions carbon trading mechanism

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Design and Application of Vague Set Theory and Adaptive Grid particle swarm optimization algorithm in Resource Scheduling optimization

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JOURNAL OF GRID COMPUTING 2023年第2期21卷 1-15页

作者： Han, Yibo Han, Pu Yuan, Bo Zhang, Zheng Liu, Lu Panneerselvam, John Nanyang Inst Technol Nanyang Inst Big Data Res Nanyang 473004 Peoples R China Nanyang Inst Technol Sch Informat Engn Nanyang 473004 Peoples R China Univ Leicester Dept Informat Univ Rd Leicester LE1 7RH England Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Peoples R China

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

关键词： Resource scheduling Vague set theory particle swarm optimization algorithm Model Multi objective

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Acceleration harmonic identification for an electro-hydraulic shaking table based on the Simulated Annealing-particle swarm optimization algorithm

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JOURNAL OF VIBRATION AND CONTROL 2024年第1-2期30卷 193-204页

作者： Yao, Jianjun Li, Yingzhao Yu, Xinda Liu, Yuanming Sun, Shuanghai Yan, Yukun Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Engn Univ Coll Mech & Elect Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

Nonlinear factors exist in electro-hydraulic shaking table and may distort the response signal when a sinusoidal acceleration is inputted. To obtain accurate harmonics, a harmonic identification strategy for the acceleration of the electro-hydraulic shaking table based on the SA-PSO (Simulated Annealing-particle swarm optimization) algorithm was proposed. First, the criterion function of harmonic identification was built with the least square method. Then, the PSO algorithm was introduced into the internal circulations of the SA algorithm to speed up the local search speed to construct the harmonic identification strategy. To verify the performance of the identification strategy, the SA algorithm, PSO algorithm, and SA-PSO algorithm were successively used to identify the response signal. And the convergence rate, accuracy, and stability of the three algorithms were compared. Finally, the harmonic regeneration was carried out after the amplitude and phase of each harmonic were obtained. Experimental results show that the harmonic identification strategy has fast identification speed and high identification accuracy.

关键词： electro-hydraulic shaking table Simulated Annealing algorithm particle swarm optimization algorithm harmonic identification criterion function least square method optimization harmonic regeneration

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A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

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IET RENEWABLE POWER GENERATION 2021年第7期15卷 1436-1450页

作者： Farh, Hassan M. H. Eltamaly, Ali M. Al-Shaalan, Abdullah M. Al-Shamma'a, Abdullrahman A. King Saud Univ Elect Engn Dept Coll Engn Riyadh 11421 Saudi Arabia King Saud Univ Sustainable Energy Technol Ctr Riyadh Saudi Arabia Mansoura Univ Elect Engn Dept Mansoura Egypt KA CARE Energy Res & Innovat Ctr Renewable Energy Dept Riyadh 11451 Saudi Arabia

In this study, a new distributed generation sizing that inherits the allocation strategy for the IEEE 30-bus benchmark system is proposed. The hybrid crow search-particle swarm optimization technique would select the optimal buses through the automatic cancelation of the unfeasible load buses. Additionally, it performs sizing that inherits allocation for minimum costs and transmission losses. Also, it selects the best distributed generation technology among wind turbines and photovoltaic energy systems based on the minimum total capital cost. The proposed strategy outperformed others in terms of total cost alleviation, power losses reduction, voltage profile improvement, and lines loading mitigation.

关键词： particle swarm optimisation minimum total capital cost photovoltaic energy systems Optimisation techniques minimum costs automatic cancelation Wind power plants wind turbines optimisation renewable distributed generations unfeasible load buses particle swarm optimization algorithm distributed generation technology Power system management, operation and economics IEEE 30-bus benchmark system optimal buses distributed power generation inherits the allocation strategy total cost alleviation inherits allocation strategy hybrid crow search-particle swarm optimization technique Distributed power generation

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An image encryption scheme based on particle swarm optimization algorithm and hyperchaotic system

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SOFT COMPUTING 2022年第11期26卷 5409-5435页

作者： Luo, Yuling Ouyang, Xue Liu, Junxiu Cao, Lvchen Zou, Yanli Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China

Protecting digital images is crucial, and image encryption schemes based on chaotic systems have been extensively studied where image permutation is a critical process. The updating processes of particle swarm optimization algorithm have the ability to change the rules of particles motion, and it can be employed to permutate image. In this work, an image encryption scheme based on the updating processes of particle swarm optimization algorithm and hyperchaotic system is proposed. Specifically, a key generation mechanism combined with secure hash algorithm 256 hash is first introduced to generate the initial values of hyperchaotic complex Lu system. Then, the plain image is confused by the position and velocity updating processes of particle swarm optimization algorithm. In addition, an adaptive diffusion mechanism is designed and applied to the confused image to obtain the final cipher image. Simulation results and security analysis show that the proposed scheme exhibits good performance of sophisticated dynamic behavior, high sensitivity to key, and effectively resisting the typical cryptanalysis attacks, and provides an alternative to real-time image encryption application.

关键词： Image encryption Hyperchaotic system particle swarm optimization algorithm Secure hash algorithm 256 hash

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