scholarly journals A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 255
Author(s):  
Gui-Rong You ◽  
Yeou-Ren Shiue ◽  
Wei-Chang Yeh ◽  
Xi-Li Chen ◽  
Chih-Ming Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ensemble Classifier ◽  
Optimization Approach ◽  
Classifier Ensembles ◽  
Swarm Optimization ◽  
Factors Affecting

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

Improved Particle Swarm Optimization Approach for Vibration Vision Measurement

2020 ◽  
pp. 2159011
Author(s):  
Chunli Zhu ◽  
Yuan Shen ◽  
Xiujun Lei
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Estimation ◽  
Search Algorithm ◽  
Computational Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Vision Measurement ◽  
Improved Pso

Traditional template matching-based motion estimation is a popular but time-consuming method for vibration vision measurement. In this study, the particle swarm optimization (PSO) algorithm is improved to solve this time-consumption problem. The convergence speed of the algorithm is increased using the adjacent frames search method in the particle swarm initialization process. A flag array is created to avoid repeated calculation in the termination strategy. The subpixel positioning accuracy is ensured by applying the surface fitting method. The robustness of the algorithm is ensured by applying the zero-mean normalized cross correlation. Simulation results demonstrate that the average extraction error of the improved PSO algorithm is less than 1%. Compared with the commonly used three-step search algorithm, diamond search algorithm, and local search algorithm, the improved PSO algorithm consumes the least number of search points. Moreover, tests on real-world image sequences show good estimation accuracy at very low computational cost. The improved PSO algorithm proposed in this study is fast, accurate, and robust, and is suitable for plane motion estimation in vision measurement.

A novel particle swarm optimization based robust H-infinity control for rotorcrafts

2014 ◽  
Vol 31 (4) ◽  
pp. 726-741 ◽  
Author(s):  
Jiyang Dai ◽  
Jin Ying ◽  
Chang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Mutation ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
H Infinity ◽  
Content Type ◽  
Modified Particle Swarm Optimization ◽  
H Infinity Control

Purpose – The purpose of this paper is to present a novel optimization approach to design a robust H-infinity controller. Design/methodology/approach – To use a modified particle swarm optimization (PSO) algorithm and to search for the optimal parameters of the weighting functions under the circumstance of the given structures of three weighting matrices in the H-infinity mixed sensitivity design. Findings – This constrained multi-objective optimization is a non-convex, non-smooth problem which is solved by a modified PSO algorithm. An adaptive mutation-based PSO (AMBPSO) algorithm is proposed to improve the search accuracy and convergence of the standard PSO algorithm. In the AMBPSO algorithm, the inertia weights are modified as a function with the gradient descent and the velocities and positions of the particles. Originality/value – The AMBPSO algorithm can efficiently solve such an optimization problem that a satisfactory robust H-infinity control performance can be obtained.

scholarly journals Reaction Control System Optimization for Maneuverable Reentry Vehicles Based on Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hang Gui ◽  
Ruisheng Sun ◽  
Wei Chen ◽  
Bin Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Optimization ◽  
Suboptimal Control ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Reentry Vehicles ◽  
Reaction Control System

This paper presents a new parametric optimization design to solve a class of reaction control system (RCS) problem with discrete switching state, flexible working time, and finite-energy control for maneuverable reentry vehicles. Based on basic particle swarm optimization (PSO) method, an exponentially decreasing inertia weight function is introduced to improve convergence performance of the PSO algorithm. Considering the PSO algorithm spends long calculation time, a suboptimal control and guidance scheme is developed for online practical design. By tuning the control parameters, we try to acquire efficacy as close as possible to that of the PSO-based solution which provides a reference. Finally, comparative simulations are conducted to verify the proposed optimization approach. The results indicate that the proposed optimization and control algorithm has good performance for such RCS of maneuverable reentry vehicles.

A Particle Swarm Optimization Approach for Minimizing GD&T Error in Additive Manufactured Parts

2017 ◽  
Vol 7 (3) ◽  
pp. 69-80 ◽  
Author(s):  
Vimal Kumar Pathak ◽  
Amit Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Geometrical Accuracy ◽  
Bed Temperature ◽  
Design Variables ◽  
Flatness Error

This paper presents a particle swarm optimization (PSO) approach to improve the geometrical accuracy of additive manufacturing (AM) parts by minimizing geometrical dimensioning and tolerancing (GD&T) error. Four AM process parameters viz. Bed temperature, nozzle temperature, Infill, layer thickness are taken as input while circularity and flatness error in ABS part are taken as response. A mathematical model is developed for circularity and flatness error individually using regression technique in terms of process parameters as design variables. For the optimum search of the AM process parameter values, minimization of circularity and flatness are formulated as multi-objective, multi-variable optimization problem which is optimized using particle swarm optimization (PSO) algorithm and hence improving the geometrical accuracy of the ABS part.

An Particle Swarm Optimization Approach for Assembly Sequence Planning

2009 ◽  
Vol 16-19 ◽  
pp. 1228-1232 ◽  
Author(s):  
Hong Yu ◽  
Jia Peng Yu ◽  
Wen Lei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Assembly Sequence Planning ◽  
Assembly Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Sequence Planning

Assembly sequence planning (ASP) is the foundation of the assembly planning which plays a key role in the whole product life cycle. Although the ASP problem has been tackled via a variety of optimization techniques, the particle swarm optimization (PSO) algorithm is scarcely used. This paper presents a PSO algorithm to solve ASP problem. Unlike generic versions of particle swarm optimization, the algorithm redefines the particle's position and velocity, and operation of updating particle positions. In order to overcome the problem of premature convergence, a new study mechanism is adopted. The geometrical constraints, assembly stability and the changing times of assembly directions are used as the criteria for the fitness function. To validate the performance of the proposed algorithm, a 29-component product is tested by this algorithm. The experimental results indicate that the algorithm proposed in this paper is effective for the ASP.

Review on Particle Swarm Optimization Approach for Optimizing Wellbore Trajectory

2020 ◽  
pp. 290-307 ◽  
Author(s):  
Kallol Biswas ◽  
Pandian M. Vasant ◽  
Moacyr Batholomeu Laruccia ◽  
José Antonio Gámez Vintaned ◽  
Myo M. Myint
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Complex Structure ◽  
Particle Swarm ◽  
Cost Effective ◽  
Pso Algorithm ◽  
Computational Time ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Flexible Control

Due to a variety of possible good types and so many complex drilling variables and constraints, optimization of the trajectory of a complex wellbore is very challenging. There are several types of wells, such as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells. This reduction of the wellbore length helps to establish cost-effective approaches that can be utilized to resolve a group of complex trajectory optimization challenges. For efficient performance (i.e., quickly locating global optima while taking the smallest amount of computational time), we have to identify flexible control parameters. This research will try to develop a review of the various (particle swarm optimization) PSO algorithm used to optimize deviated wellbore trajectories. This chapter helps to find out optimal wellbore trajectory optimization algorithms that can close the technology gap by giving a useful method. This method can generate a solution automatically.

A Particle Swarm Optimization Approach to Fuzzy Case-based Reasoning in the Framework of Collaborative Filtering

2014 ◽  
Vol 1 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Shweta Tyagi ◽  
Kamal K. Bharadwaj
Keyword(s):  
Particle Swarm Optimization ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Case Based

The particle Swarm Optimization (PSO) algorithm, as one of the most effective search algorithm inspired from nature, is successfully applied in a variety of fields and is demonstrating fairly immense potential for development. Recently, researchers are investigating the use of PSO algorithm in the realm of personalized recommendation systems for providing tailored suggestions to users. Collaborative filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. However, data sparsity and prediction accuracy are the major concerns related to CF techniques. In order to handle these problems, this paper proposes a novel approach to CF technique by employing fuzzy case-based reasoning (FCBR) augmented with PSO algorithm, called PSO/FCBR/CF technique. In this method, the PSO algorithm is utilized to estimate the features importance and assign their weights accordingly in the process of fuzzy case-based reasoning (FCBR) for the computation of similarity between users and items. In this way, PSO embedded FCBR algorithm is applied for the prediction of missing values in user-item rating matrix and then CF technique is employed to generate recommendations for an active user. The experimental results clearly reveal that the proposed scheme, PSO/FCBR/CF, deals with the problem of sparsity as well as improves the prediction accuracy when compared with other state of the art CF schemes.

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