A New Multi-swarm Particle Swarm Optimization for Robust Optimization Over Time

2017 ◽  
pp. 99-109 ◽  
Author(s):  
Danial Yazdani ◽  
Trung Thanh Nguyen ◽  
Juergen Branke ◽  
Jin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Over Time

scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Robust optimization using multi-objective particle swarm optimization

2009 ◽  
Vol 14 (2) ◽  
pp. 174-177 ◽  
Author(s):  
Satoshi Ono ◽  
Yohei Yoshitake ◽  
Shigeru Nakayama
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective

Melanocytic Lesions Screening through Particle Swarm Optimization

2014 ◽  
pp. 355-384 ◽  
Author(s):  
Rustem Popa
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Skin Lesions ◽  
Melanocytic Nevus ◽  
Main Difficulty ◽  
Automated Diagnosis ◽  
Swarm Optimization ◽  
Melanocytic Lesions ◽  
Suspicious Lesions ◽  
Over Time

Early detection of malignant melanoma, which is the most dangerous skin cancer, significantly improves the chances of curing it. For this reason, dermatologists are looking for new methods for the examination of suspicious lesions that changes their shape over time. The author investigates in this chapter some algorithms which may be used for automated diagnosis of skin lesions. First algorithm performs the image segmentation by edge detection, which plays an important role in identifying borders of the lesion. Next algorithm uses the Particle Swarm Optimization (PSO) paradigm for recognizing the images of the same melanocytic nevus taken at different moments of time. The idea is that a novel view of an object can be recognized by simply matching it to combinations of known views of the same object. The main difficulty in implementing this idea is determining the parameters of the combination of views. The space of parameters is very large and we propose a PSO approach to search this space efficiently. The effectiveness of this approach is shown on a set of real images captured with a camera under different angles of view.

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.

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