scholarly journals A Strongly Interacting Dynamic Particle Swarm Optimization Method

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
S. Kok ◽  
J. A. Snyman
Keyword(s):  
Particle Swarm Optimization ◽  
Equations Of Motion ◽  
Particle Swarm ◽  
Unconstrained Minimization ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Lower Function ◽  
Interacting Particle ◽  
Strongly Interacting

A novel dynamic interacting particle swarm optimization algorithm (DYN-PSO) is proposed. The algorithm can be considered to be the synthesis of two established trajectory methods for unconstrained minimization. In the new method, the minimization of a function is achieved through the dynamic motion of a strongly interacting particle swarm, where each particle in the swarm is simultaneously attracted by all other particles located at positions of lower function value. The force of attraction experienced by a particle at higher function value due to a particle at a lower function value is equal to the difference between the respective function-values divided by their stochastically perturbed position difference. The resultant motion of the particles under the influence of the attracting forces is computed by solving the associated equations of motion numerically. An energy dissipation strategy is applied to each particle. The specific chosen force law and the dissipation strategy result in the rapid collapse (convergence) of the swarm to a stationary point. Numerical results show that, in comparison to the standard particle swarm algorithm, the proposed DYN-PSO algorithm is promising.

Particle Swarm Optimization Method for Panel Layout

2010 ◽  
Vol 450 ◽  
pp. 308-311 ◽  
Author(s):  
Yu Qing Xu ◽  
Zhi Yang ◽  
Qing Xin Meng
Keyword(s):  
Particle Swarm Optimization ◽  
Nuclear Power ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Operational Conditions ◽  
Frequency Sequence

Panel layout in control room of nuclear power plant requires satisfying layout principles and requirement for multiple operational conditions. Traditional panel layout methods are difficult to integrate these principles; the layout scheme heavily depends on designers’ individual judgments and experience. So the randomness of layout scheme is hard to overcome. Particle swarm optimization is a global efficient solution to complex optimization problems with multi-constraints. First, the operational efficiency is selected as optimization object. Second, objective function is constructed according to the evaluating indexes which include operation frequency, sequence and importance of components. Third, Particle swarm algorithm was used to optimize the layout scheme. A case study of layout optimization design is conducted to illustrate the proposed method.

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

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

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