Software module clustering using particle swarm optimization

2018 ◽  
pp. 581-601
Author(s):  
Amarjeet Prajapati ◽  
Jitender Kumar Chhabra
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Software Module ◽  
Swarm Optimization ◽  
Software Module Clustering

scholarly journals Software Module Clustering Using Grid-Based Many-Objective Particle Swarm Optimization

2021 ◽  
Author(s):  
Amarjeet Prajapati
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Poor Performance ◽  
Software Module ◽  
Swarm Optimization ◽  
Software Module Clustering ◽  
The Many ◽  
Grid Based

Abstract The poor performance of traditional multi-objective optimization algorithms over the many-objective optimization problems has led to the development of a variety of many-objective optimization algorithms. Recently, several many-objective optimization algorithms have been proposed to address the different class of many-objective optimization problems. Most of the existing many-objective optimization algorithms were designed from the perspective of synthetic many-objective optimization problems. Despite the tremendous work made in the development of the many-objective optimization algorithms for solving the synthetic many-objective optimization problems, still real-world many-objective optimization problems gained little attention. In this work, we propose a grid-based many-objective particle swarm optimization (GrMaPSO) for the many-objective software optimization problem. In this contribution, the grid-based selection strategies along with other supportive strategies such as two-archive storing and crowding distance have been exploited in the framework of particle swarm optimization. The performance of the proposed approach is evaluated and compared to three existing approaches over five problem instances. The results demonstrate that the proposed approach is more effective and has significant advantages over existing many-objective approaches designed for the software module clustering problems.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear

Particle Swarm Optimization with Hierarchical Structure

2009 ◽  
Vol 129 (3) ◽  
pp. 568-569
Author(s):  
Satoko Kinoshita ◽  
Atsushi Ishigame ◽  
Keiichiro Yasuda
Keyword(s):  
Particle Swarm Optimization ◽  
Hierarchical Structure ◽  
Particle Swarm ◽  
Swarm Optimization
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