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CLASSIFICATION OF EDIBLE OILS BY INFRARED SPECTROSCOPY WITH OPTIMIZED K-MEANS CLUSTERING BY A HYBRID particle swarm algorithm

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ANALYTICAL LETTERS 2013年第17期46卷 2727-2738页

作者： Ren Haixia Lin Weiqi Shi Weimin Shen Qi Zhengzhou Univ Coll Chem & Mol Engn Zhengzhou 450052 Peoples R China Xiamen Prod Qual Supervis & Inspect Inst Xiamen Peoples R China

The K-means algorithm has some limitations including dead-unit properties, heavy dependence on the initial choice of cluster centers, convergence to local optima, and sensitivity to the number of clusters. This paper presents an efficient algorithm that optimizes K-means clustering by a hybrid particle swarm algorithm. The modified discrete algorithm is used to select variables and is continuously applied to update cluster centers simultaneously. The nearest center classification is then employed to classify the test samples. The proposed algorithm was applied to discriminate various edible oil varieties by employing Fourier transform infrared spectroscopy. As a comparison, the common K-means clustering, principal component analysis, and partial least squares techniques were also applied to classify these edible oil samples. Results demonstrated that the proposed method is an accurate and rapid strategy for identifying edible oils.

关键词： Edible oil FT-IR spectra K-means algorithm particle swarm algorithm

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Classification Method of Marine Tourism Resource of Least Square Support Vector Machines Based on particle swarm algorithm

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JOURNAL OF COASTAL RESEARCH 2018年第Spe83期000卷 632-636页

作者： Xu, Huali Fu, Maosheng Jia, Chaochuan West Anhui Univ Sch Elect & Informat Engn Luan 237012 Peoples R China

In the current marine tourism resources classification research, the potential relationship between various types of marine tourism resources characteristics can not be fully used. The dimensional Curse and low accuracy can not be solved in the process of marine tourism resources classification. Thus, this article proposes a method for automatically selecting parameters of support vector machine based on particle swarm optimization and applies it on marine tourism resources classification. This method maps the input space of marine tourism resources to a high-dimensional feature space through non-linear transformation and finds the optimal linear classification surface of marine tourism resources classification in this new space. this method takes the classification accuracy as the fitness function of optimization problem and uses the particle swarm optimization algorithm to optimize parameters of support vector machine.

关键词： particle swarm algorithm least square support vector machines marine tourism resources resource classification method

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Fuzzy logic controller parameter tuning using particle swarm algorithm

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2009年第1期6卷 26-31页

作者： Omizegba, Elijah Ehiagwina Monikang, Fidelis Azoa Abubakar Tafawa Balewa Univ Elect & Elect Engn Programme PMB 0248 Bauchi Bauchi State Nigeria

Fuzzy logic models of plants are attractive because they have structures with physical meanings that can be used to predict the performance as well as provide insight into the workings of the modelled system. However, the initial definitions of fuzzy model parameters are subjective, heuristic and therefore, inaccurate. In this paper, the particle swarm optimisation algorithm is used to find the optimum fuzzy relation matrix of a new fuzzy reasoning model for the purpose of modelling. Simulation results show that a good fuzzy model of complex systems can be obtained with no expert input.

关键词： modelling particle swarm algorithm PSA fuzzy control parameter tuning

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Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Hu, Zhuo Guo, Weihao Zhou, Kege Wang, Lei Wang, Fu Yuan, Jinliang Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China China Coal Soc Beijing 100013 Peoples R China

This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air-fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air-fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

关键词： Dual-fuel engine particle swarm algorithm PID control Air-fuel ratio Fuel replacement ratio

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A particle swarm algorithm for high dimensional, multi-optima problem spaces.

A Particle Swarm algorithm for high dimensional, multi-optim...

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IEEE swarm Intelligence Symposium

作者： Hendtlass, T Swinburne Univ Technol Ctr Intelligent Syst & Complex Proc Fac Informat & Commun Technol Hawthorn Vic 3122 Australia

ISBN: (纸本)0780389166

The same mechanisms that are so efficient at finding optima may result in a conventional particle swarm Optimisation (PSO) algorithm becoming trapped in a local optimum and unable to escape from this to search for further, hopefully better, optima. This problem becomes more significant as the dimensionality of the problem space increases. A new algorithm that uses Waves of swarm particles (WoSP) is introduced that allows a swarm to escape from an optimum and forces it to go on exploring. Results are given for a deceptive problem in both 30 and 100 dimensions. The WoSP algorithm performs well on these problems, encouraging the application of WoSP to other multi-optima high dimensionality problems.

关键词： particle swarm optimisation search problems local optimum multioptima high dimensionality problem particle swarm optimization waves of swarm particle tabu search particle swarm optimization local optimum particle swarm algorithm dike swarms

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Investigation of Potential Parameters Effect on Stable Structures of Pt-Pd Alloy Nanoparticles by particle swarm algorithm 32

Investigation of Potential Parameters Effect on Stable Struc...

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32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Shao, Guifang Zhangr, Junfa Li, Tiejun Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Jimei Univ Coll Informat Sci Xiamen 361021 Peoples R China

ISBN: (纸本)9781538629017

Because the structure determines the chemical and physical properties, atomic-level understanding of structural features of bimetallic nanoparticles (NPs) is of great importance to their syntheses and applications. In this work, we propose an improved discrete particle swarm optimization (PSO) algorithm to predict the stable structures of bimetallic NPs. In this algorithm, the variable pair of 0 and 1 (which relatively represent the two atoms) is introduced to enhance the speed of searching optimization in the initial stages, and the simulated annealing operator is added to avoid premature convergence and trapping into local optimal solution. Tetrahexahedral (THH) Pt-Pd bimetallic NPs containing 3285 atoms are used to test the effectiveness of the proposed algorithm. Furthermore, the proposed method is employed to investigate and compare the stable structures of the NPs by changing some parameters of the Gupta potentials. The results have demonstrated the superior convergence of the proposed PSO algorithm in structural prediction of bimetallic NPs. The comparison of the stable structures based on three groups of the parameters in Gupta potentials shows that for the parameter I, the structure is in best agreement with the experimental result, while for the parameter III, the Pt-Pd bimetallic NPs is most stable.

关键词： Nanoparticle Stable structures particle swarm algorithm Simulated annealing Potential

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Cutting Parameter Optimization of Stability in Cylindrical Shell Based on particle swarm algorithm

Cutting Parameter Optimization of Stability in Cylindrical S...

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International Conference on Nanotechnology and Precision Engineering (ICNPE 2012)

作者： Chen, Dong Lin, Bin Han, Zhilin Zhang, Yanbin Tianjin Univ Tianjin Key Lab Equipment Design & Mfg Technol Tianjin 300072 Peoples R China

ISBN: (纸本)9783037856406

For cylindrical shell, because of its structure characteristic (i.e. long and thin-walled), it has low stiffness, bad fabrication procedure. And it's very easy to cause vibration in the cutting process, especially the type of regenerative vibration. So, it's vital for the surface quality of work-piece and cutting stability to select cutting parameter reasonably. The paper proposed a dynamic optimization method, which aimed at maximization of material removal rate. This method could guarantee the maximum material removal rate under the condition of cutting stability based on particle swarm algorithm (PSA). In the end, it verified the optimization results by the experiments.

关键词： particle swarm algorithm Cutting stability Parameters optimization Cylindrical shell

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A particle swarm algorithm for assembly sequence planning

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International Conference on Manufacturing Science and Engineering (ICMSE 2009)

作者： Xing, Yanfeng Wang, Yansong Zhao, Xiaoyu Shanghai Univ Engn Sci Shanghai 201620 Peoples R China

ISBN: (纸本)9780878492800

A particle swarm algorithm is proposed to generate optimal assembly sequences for compliant assemblies. Firstly, the liaison graph and the adjacency matrix describe the geometry of the compliant assemblies. An assembly sequence is represented by a character string, whose length is the number of all parts. The conceptual tolerance analysis is used to evaluate feasible sequences. Thereafter, the particle swarm algorithm is presented to generate assembly sequences, in which the elite ratio is applied to improve optimization results. Finally a fender assembly is used to illustrate the algorithm of assembly sequence generation and optimization.

关键词： Assembly sequence compliant assembly particle swarm algorithm optimization

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Optimal Reactive Power Planning Based on Simulated Annealing particle swarm algorithm Considering Static Voltage Stability

Optimal Reactive Power Planning Based on Simulated Annealing...

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International Conference on Intelligent Computation Technology and Automation

作者： Mao Yingni Li Maojun Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9780769533575

Introducing voltage stability into optimal reactive power planning, and a mathematical model of multi-objective optimal planning for reactive power is established, in which the network loss, investment Of reactive compensation equipment, voltage level and static voltage stability margin are considered In this paper, firstly singular value decomposition method is applied to identify weak bus as candidate nodes for installing new reactive compensation equipment;then the optimal nodes and capacity of reactive power compensation are obtained by solving the model with fuzzy method and simulated annealing particle swarm optimization(SA-PSO) algorithm. The proposed method has significant improvement in decreasing network loss, improving voltage stability and voltage quality of the whole power system, which is proved by the simulation results of IEEE 14-node system.

关键词： Voltage stability Power system planning reactive compensation Reactive power network losses electrostatic pressure fuzzy method Voltage Quality particle swarm algorithm stability margin simulated annealing power systems

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An improved nonlinear fitting method and its application in function approximation based on particle swarm algorithm 7

An improved nonlinear fitting method and its application in ...

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7th International Conference Intelligent Computation Technology Automation (ICICTA)

作者： Xiao Fei Liu Qiang Jia Bei Wu Zeping Xian Commun Inst Xian 710000 Peoples R China Natl Univ Def Technol Coll Aerosp & Mat Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479966363

Standard normal distribution is widely used in engineering. But it is not so convenient during application, because the analytic expression of the distribution function does not exist. In this paper, the particle swarm algorithm is applied in the nonlinear fitting process of standard normal distribution, and different modified methods are used to obtain analytical expressions of standard normal distribution function. According to the results, it is approved that the improved fitting method can get a better precision.

关键词： nonlinear fitting method particle swarm algorithm function approximation

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