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24th Chinese Control and Decision Conference (CCDC)

作者： Zhou Wei Bu Yan-ping E China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9781457720727

Based on cultural algorithm and classical particle swarm optimization (PSO) algorithm, a cultural particle swarm optimization (CPSO) algorithm is proposed. In the improved algorithm, double evolutionary mechanisms are used. The population space and the belief space of cultural algorithm are redesigned. The proposed model was used to solve the partner selection problem of virtual enterprise. In a virtual enterprise, the whole task can be accomplished by the cooperation among those candidate partners. The optimal objective is to minimize the total cost and completing time. Finally, the performance of the algorithm is evaluated by simulations. Results demonstrate the feasibility and efficiency of the proposed algorithm.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Simulating particle swarm optimization algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

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Journal of Mathematics and System Science 2015年第10期5卷 399-410页

作者： Basad Ali Hussain Al-sarray Laboratoire de Mathematiques de Besancon Universite de Franche-Comte Besancon cedex France.

This paper present a simulation study of an evolutionary algorithms, particle swarm optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇***＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

关键词： particle swarm optimization algorithm Likelihood function ARMA（1 1） Model

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Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining particle swarm optimization algorithm and Artificial Neural Network

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BIG DATA AND COGNITIVE COMPUTING 2024年第3期8卷 33页

作者： Molaei, Sepideh Cirillo, Stefano Solimando, Giandomenico Tarbiat Modares Univ Fac Elect & Comp Engn Tehran 14115111 Iran Univ Salerno Dept Comp Sci I-84084 Salerno Italy

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

关键词： cancer diagnosis neural network particle swarm optimization algorithm microRNAs

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Research on hybrid strategy particle swarm optimization algorithm and its applications

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Yao, Jicheng Luo, Xiaonan Li, Fang Li, Ji Dou, Jundi Luo, Hongtai Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Ctr Appl Math Guangxi GUET Guilin 541004 Peoples R China Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional particle swarm optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy particle swarm optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly optimization algorithm (BOA), Ant Colony optimization (ACO), and Firefly algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

关键词： particle swarm optimization algorithm Hybrid strategy Adaptive weight adjustment Reverse learning strategy Cauchy mutation mechanism Hook-Jeeves strategy Feature selection

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Application of varying population size particle swarm optimization algorithm to AGC of power systems

Application of varying population size particle swarm optimi...

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6th World Congress on Intelligent Control and Automation

作者： Ma, Fei Chen, Xue-bo Anshan Univ Sci & Technol Sch Elect & Informat Engn Anshan 114044 Peoples R China

ISBN: (纸本)1424403316

An effective method of making tradeoff between the optimize precision and optimize speed for load frequency control in the automatic generation control, which can improve the calculating process of particle swarm algorithm is presented in this paper. This method which is suit for the case that the object to be optimized is complicate can be used to accelerate optimizing process and save calculate time but not influence precision due to the fact that particle swarm optimization algorithm is not sensitive to the number of particles. The method of optimizing PI controller coefficient using promoted particle swarm algorithm which is used to meet the different performance need in single-area and two-area interconnected power system is proposed respectively. The simulation result shows that the performance is better than the PI controller optimized by genetic algorithm.

关键词： automatic generation control interconnected power systems particle swarm optimization algorithm genetic algorithm

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On multi-population parallel particle swarm optimization algorithm

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26th Chinese Control Conference

作者： Zhang Dingxue Guan Zhihong Liu Xinzhi Huazhong Univ Sci & Technol Control Sci & Engn Dept Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9787811240559

To improve performance of particle swarm optimization (PSO) algorithm and avoid trapping to local minima, a multi-population parallel particle swarm optimization (DPPSO) algorithm is proposed. In the algorithm, sub populations are divided into exploration and exploitation types. The global version PSO is used in the exploration population to enhance ability of exploring the best individual, and the local version PSO is used in the exploitation population to enhance ability of local search and find the best global result in the local range. Simultaneously, keep communication with sub populations in running. The experimental results show that the restraining premature convergence is enhanced for maintaining the individual diversity.

关键词： multi-population parallel algorithm particle swarm optimization algorithm

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A greedy particle swarm optimization (GPSO) algorithm for testing real-world smart card applications

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2020年第2期22卷 183-194页

作者： Allawi, Hamzeh M. Al Manaseer, Waref Al Shraideh, Mohammad Univ Jordan Dept Comp Sci Amman Jordan

Software testing continues to be regarded as a necessary and critical step in the software development life cycle. Among the multitudes of existing techniques, particle swarm optimization (PSO) algorithm, in particular, has shown superior merits for automatically generating software test cases for its easy implementation and for relying on fewer parameters that require tuning. Hence, several state-of-the-art PSO-based algorithms have been successfully used as a test data generator. On the other hand, greedy-based algorithms, which are commonly used to solve complex and multi-step combinatorial problems, are starting to gain momentum as a solution for the complexity problem of software testing. Greedy algorithms favored over other techniques when the solution of the problem is guaranteed to be near-optimal. As a result, the utilization of both greedy and PSO algorithms in a single solution for automatically generating test data represents a strong candidate if designed carefully. In this paper, we propose a novel hybrid greedy and PSO algorithm (GPSO) that jointly guarantees the effectiveness and close to optimality results for generating a minimum number of test data. Compared with the widely employed genetic algorithm (GA), our proposed GPSO outperforms the GA in terms of the average number of iterations, execution time, and coverage percentage. Experimental trials with six different typical Java card applications show that the use of the proposed GPSO as a test data generator results in an outstanding performance.

关键词： Software testing Java smart card particle swarm optimization algorithm Greedy algorithm Genetic algorithm

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A particle swarm optimization algorithm with Time Varying Parameters

A Particle Swarm Optimization Algorithm with Time Varying Pa...

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第30届中国控制与决策会议

作者： Zhen Hu Dexuan Zou Zhi Kong Xin Shen School of Electrical Engineering and Automation Jiangsu Normal University School of Electrical Engineering and Automation Northeastern University at Qinhuangdao

A particle swarm optimization(TVPSO) algorithm with time varying parameters is proposed to improve the performance of particle swarm optimization(PSO) algorithm by two improvements. Aiming at the fact general PSO algorithms have the disadvantages of falling into local optima caused by linearly decreased inertia weight. TVPSO uses the related properties of the trigonometric function to improve the dynamic changes of inertia weight along With Time. The inertia weight maintains a large value in the initial stage, and decreases gradually and reaches a small value at the end. Thus, the global search capability and convergence performance were improved;In order to cope with changes in inertia weight, learning factors also change with time. TVPSO and the other latest particle swarm optimization algorithms are tested on 10 functions at the same time. Experimental results show that TPSO has faster search speed and stronger global search capabilities.

关键词： Time varying parameters particle swarm optimization algorithm Trigonometric function

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Fault Diagnosis Analysis with Support Vector Regression and particle swarm optimization algorithm

Fault Diagnosis Analysis with Support Vector Regression and ...

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2010 Chinese Control and Decision Conference

作者： WenJie Tian,JiCheng Liu Beijing Automation Institute of Beijing Union University,Beijing,China,100101

The fault diagnosis model with support vector regression(SVR) and particle swarm optimization algorithm (POSA) for is *** novel structure model has higher accuracy and faster convergence *** construct the network structure,and give the algorithm *** impact factor of fault behaviors is *** the ability of strong self-learning and faster convergence,this fault detection method can detect various fault behaviors rapidly and effectively by learning the typical fault characteristic *** the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction,the reduces of the original dataset are calculated and used to train individual SVR for ensemble,and consequently,increase the detection *** validate the effectiveness of the proposed method,simulation experiments are performed based on the electronic circuit *** results show that the proposed method is a promised method owning to its high diversity,high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction particle swarm optimization algorithm

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Integration of particle swarm optimization and genetic algorithm for dynamic clustering

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INFORMATION SCIENCES 2012年 195卷 124-140页

作者： Kuo, R. J. Syu, Y. J. Chen, Zhen-Yao Tien, F. C. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Vanguard Int Semicond Corp Hsinchu Taiwan De Lin Inst Technol Dept Business Adm New Taipei City Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei Taiwan

Although the algorithms for cluster analysis are continually improving, most clustering algorithms still need to set the number of clusters. Thus, this study proposes a novel dynamic clustering approach based on particle swarm optimization (PSO) and genetic algorithm (GA) (DCPG) algorithm. The proposed DCPG algorithm can automatically cluster data by examining the data without a pre-specified number of clusters. The computational results of four benchmark data sets indicate that the DCPG algorithm has better validity and stability than the dynamic clustering approach based on binary-PSO (DCPSO) and the dynamic clustering approach based on GA (DCGA) algorithms. Furthermore, the DCPG algorithm is applied to cluster the bills of material (BOM) for the Advantech Company in Taiwan. The clustering results can be used to categorize products which share the same materials into clusters. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Cluster analysis Dynamic clustering particle swarm optimization algorithm Genetic algorithm

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