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检索条件"主题词=Particle Swarm algorithm"

共 736 条记录，以下是171-180 订阅

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The shortest path optimization based on mutation particle swarm optimization algorithm 4

The shortest path optimization based on mutation particle sw...

引用

4th International Conference on Intelligent System and Applied Material (GSAM)

作者： Li, Juan Wuhan Technol & Business Univ Wuhan 430065 Peoples R China

ISBN: (纸本)9783038353140

The traditional evolutionary algorithm is cannot converge faster to solve the path optimization problems, and the path that is computed is not the shortest path, in allusion to the disadvantage of this algorithm, a mutation particle swarm optimization algorithm is proposed. The algorithm introduces the adaptive mutation strategy, and accelerated the speed to search for the global optimal solution. For seven examples experiment in standard database, the result shows that the algorithm is more efficient..

关键词： The shortest path problem particle swarm algorithm adaptive

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Research on Robot Path Planning Technology by Integrating particle swarm Optimization algorithm and DWA algorithm 10

Research on Robot Path Planning Technology by Integrating Pa...

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10th International Conference on Control, Automation and Robotic (ICCAR)

作者： Fu, Sanli Huang, Hengyi Wang, Shifeng Univ Sanya Hainai 572000 Peoples R China

ISBN: (纸本)9798350373172;9798350373189

Path planning technology is one of the key technologies for robots to achieve autonomous navigation and complete transportation tasks. This article mainly analyzes and studies two commonly used intelligent bio-inspired path planning algorithms: particle swarm Optimization (PSO) algorithm and Dynamic Window Approach (DWA) algorithm. We have made improvements and optimizations to both algorithms, and designed a fusion algorithm that combines their advantages. The paper focuses on studying the global path of the PSO algorithm, followed by research on the local path using the DWA algorithm. Finally, we tested the improved PSO-DWA fusion algorithm using MATLAB. The test results show that this fusion algorithm can obtain the global optimal path by utilizing known global information while effectively avoiding unknown dynamic obstacles. It can better accomplish path planning tasks in practical situations with superior performance, improve robot safety during task execution, and ensure subsequent execution efficiency.

关键词： Path planning particle swarm algorithm DWA Fusion algorithm Intelligent biomimetic path

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The Clustering algorithm Based on particle swarm Optimization algorithm

The Clustering Algorithm Based on Particle Swarm Optimizatio...

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International Conference on Intelligent Computation Technology and Automation

作者： Pei Zhenkui Hua Xia Han Jinfeng China Univ Petr Coll Comp & Commun Engn Dongying 257061 Peoples R China

ISBN: (纸本)9780769533575

After analyzing the disadvantages of the classical K-means clustering algorithm, this paper combines the core idea of K-means clustering method with PSO algorithm and proposes a new clustering method which is called clustering algorithm based on particle swarm Optimization algorithm. It used the global optimization of PSO algorithm to make up the shortage of the clustering method. The algorithm is evaluated on Iris plants database, Results show that the algorithm is more effective and promising.

关键词： Clustering methods Clustering algorithms particle swarm algorithm algorithms particle swarm optimization Global optimization parametric subharmonic oscillator Iris Plant

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An Evolutionary particle swarm Optimization algorithm for Data Clustering

An Evolutionary Particle Swarm Optimization Algorithm for Da...

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IEEE swarm Intelligence Symposium

作者： Alam, Shafiq Dobbie, Gillian Riddle, Patricia Univ Auckland Dept Comp Sci Auckland 1 New Zealand

ISBN: (纸本)9781424427048

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas such as finding similarities in images, text data and bio-informatics data. Various optimization techniques have been proposed to improve the performance of clustering algorithms. In this paper we propose a novel algorithm for clustering that we call Evolutionary particle swarm Optimization (EPSO)-clustering algorithm which is based on PSO. The proposed algorithm is based on the evolution of swarm generations where the particles are initially uniformly distributed in the input data space and after a specified number of iterations;a new generation of the swarm evolves. The swarm tries to dynamically adjust itself after each generation to optimal positions. The paper describes the new algorithm the initial implementation and presents tests performed on real clustering benchmark data. The proposed method is compared with k-means clustering- a benchmark clustering technique and simple particle swarm clustering algorithm. The results show that the algorithm is efficient and produces compact clusters.

关键词： particle swarm optimization algorithms dike swarms Clustering algorithms Cluster Analysis particle swarm algorithm BENCHMARKS data clustering program area incoming data Evolutionary

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The Identification Strategy of Bouc-Wen Model Based on Improved particle swarm Optimization algorithm 33

The Identification Strategy of Bouc-Wen Model Based on Impro...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Li, Zicheng Xu, Ruirui Wang, Hou-Neng Xiong, Tao Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

ISBN: (纸本)9781665440899

The piezoelectric micro-positioning system consists of a piezoelectric actuator that operates a positioning system. Hysteresis nonlinearity is one of the significant variables limiting the positioning precision of these stages. This paper proposes an asymmetrical Bouc-Wen (B-W) model to describe the hysteresis of piezoelectric actuator. In order to solve the problem of low parameter identification accuracy, an improved particle swarm optimization (IPSO) algorithm IS presented. The inertial weight and learning factor nonlinear change strategy is proposed to quickly find the optimal model parameters. The results show that the algorithm has better overall searching ability in the early stage and faster convergence speed compared with the standard particle swarm optimization (PSO). The proposed algorithm can improve the solution accuracy and accelerate the global convergence.

关键词： Bouc-Wen Model particle swarm algorithm Parameter Identification Hysteresis Nonlinearity

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A Novel Classification Approach Based on Support Vector Machine and Adaptive particle swarm Optimization algorithm

A Novel Classification Approach Based on Support Vector Mach...

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International Symposium on Knowledge Acquisition and Modeling

作者： Chen, Xi Han, Jing Northeastern Univ Sch Business Adm Shenyang 110004 Liaoning Peoples R China Shanxi Co China Mobile Grp Xian 710075 Peoples R China

ISBN: (纸本)9780769534886

In this article we describe a novel Adaptive particle swarm optimization (APSO) algorithm based on population diversity information. It is presented to solve the precocious convergence problem of particle swarm optimization algorithm. The APSO algorithm uses the information of the population diversity to adjust nonlinearly inertia weight. Velocity mutation factor and position interchange factor are both introduced and the global performance is clearly improved The APSO algorithm is applied to optimization of parameters in the optimal model based on support vector machine (SVM. SVM is a popular classification method with many diverse applications. A novel Adaptive particle swarm optimization (APSO) based approach for parameter determination and feature selection of the SVM, termed APSO+SVM is developed. The illustrating example shows that the classification accuracy of APSO+SVM is higher than other traditional methods of classification, so using APSO+SVM method to classify is feasible and effective.

关键词： Adaptive particle swarm Optimization (APSO) Support Vector Machine (SVM) adaptive variance classification Support Vector Network particle swarm algorithm particle swarm optimization Parameter optimization Classification Methods Self tuning Feature extraction

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An optimization of 15.12 GHz SPDT switch using particle swarm optimization algorithm

An optimization of 15.12 GHz SPDT switch using particle swar...

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4th International Conference on Intelligent and Advanced Systems (ICIAS) and A Conference of World Engineering, Science and Technology Congress (ESTCON)

作者： Lau, T. L. Neoh, S. C. Marzuki, A. Univ Malaysia Perlis Sch Microelect Engn Perlis Malaysia Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia

ISBN: (纸本)9781457719677

In this paper, a simulation-based particle swarm optimization (PSO) approach is developed to optimize the operational parameters of a 15.12 GHz single pole double throw (SPDT) switch. The Agilent Advance Design System (ADS) is used to synthesize the performance of the switch design while PSO is employed as the optimizer for achieving the required switch performance. The performance of the designed SPDT switch is assessed in terms of insertion loss and isolation. Simulation results show that the proposed component values of SPDT switch is manage to fulfill the required specifications, achieving 25.978dB of isolation loss and 3.077dB of insertion loss.

关键词： single pole double throw switch insertion loss single-pole double-throw (SPDT) particle swarm algorithm particle swarm optimization switching characteristics

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An Improved particle swarm Optimization algorithm for Irregular Flight Recovery Problem 13th

An Improved Particle Swarm Optimization Algorithm for Irregu...

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13th International Conference on swarm Intelligence (ICSI)

作者： Zhou, Tianwei He, Pengcheng Zhang, Churong Lai, Yichen Zhong, Huifen Wu, Xusheng Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Shenzhen Univ Great Bay Area Int Inst Innovat Shenzhen 518060 Peoples R China Univ Macau Fac Business & Adm Macau 999078 Peoples R China Shenzhen Hlth Dev Res & Data Management Ctr Shenzhen 518060 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper.

关键词： Crew recovery Irregular flight particle swarm algorithm Cross-over mechanism Mutation mechanism

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Modified particle swarm optimization algorithm for engineering structural optimization problem 13

Modified particle swarm optimization algorithm for engineeri...

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Ren Yanzhi Liu Sanyang Xidian Univ Sch Math & Stat Xian Shaanxi Peoples R China

ISBN: (纸本)9781538648223

This paper introduces a modified particle swarm optimization algorithm (MPSO) to solve the engineering structural optimization problems. In order to balance the objective function value and constraint violation of particles, we present an improved adaptive fitness method. The teaching mechanism is used to guide the population evolution in feasible region, and the algorithm uses the adaptive inertial weight and learning factors to balance exploration and exploitation. Finally, the particle reset strategy can avoid the algorithm into local optimum effectively. The algorithm was validated using two benchmark engineering optimization problems. Comparison of obtained computation results with other existing optimization methods shows the superiority of the MPSO in terms of accuracy.

关键词： particle swarm algorithm learning mechanism engineering optimization problems adaptive fitness transformation

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Integration of an Improved particle swarm Optimization algorithm and Fuzzy Neural Network for Shanghai Stock Market Prediction

Integration of an Improved Particle Swarm Optimization Algor...

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Workshop on Power Electronics and Intelligent Transportation System

作者： Fu-Yuan, Huang S China Univ Technol Sch Econ & Commerce Guangzhou 510640 Peoples R China

ISBN: (纸本)9780769533421

particle swarm Optimization (PSO) algorithm and Fuzzy Neural Network (FNN) system has been widely used to solve complex decision making problems in practice. However, both of them more or less suffer from the slow convergence, "black-box" and occasionally involve in a local optimal solution. To overcome these drawbacks of PSO and FNN, in this study an improved particle swarm optimization algorithm (IPSO) is developed and then combined with fuzzy neural network to optimize the network training process. Furthermore, the new IPSO-FNN model has been applied to Shanghai stock market prediction problem, and the results indicate that the predictive accuracies obtained from IPSO-FNN are much higher than the ones obtained from neural network system(NNs). To make this clearer, an illustrative example is also demonstrated in this study. It seems that the proposed new comprehensive evolution algorithm may be an efficient forecasting system in financial time series analysis.

关键词： Fuzzy neural networks time series analysis particle swarm optimization parametric subharmonic oscillator DNA Integration integration evolutionary algorithm particle swarm algorithm

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