Nonlinear Time-Varying Stability Analysis of Particle Swarm Optimization

2010 ◽  
Author(s):  
Weibing Fan ◽  
Zhihua Cui ◽  
Yongjing Chen ◽  
Ying Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Stability Analysis ◽  
Particle Swarm ◽  
Time Varying ◽  
Swarm Optimization

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

scholarly journals Path Planning for Mobile Objects in Four-Dimension Based on Particle Swarm Optimization Method with Penalty Function

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yong Ma ◽  
M. Zamirian ◽  
Yadong Yang ◽  
Yanmin Xu ◽  
Jing Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Penalty Function ◽  
Particle Swarm ◽  
Goal Function ◽  
Optimization Method ◽  
Mobile Object ◽  
Time Varying ◽  
Swarm Optimization ◽  
Calculus Of Variation ◽  
Mobile Objects

We present one algorithm based on particle swarm optimization (PSO) with penalty function to determine the conflict-free path for mobile objects in four-dimension (three spatial and one-time dimensions) with obstacles. The shortest path of the mobile object is set as goal function, which is constrained by conflict-free criterion, path smoothness, and velocity and acceleration requirements. This problem is formulated as a calculus of variation problem (CVP). With parametrization method, the CVP is converted to a time-varying nonlinear programming problem (TNLPP). Constraints of TNLPP are transformed to general TNLPP without any constraints through penalty functions. Then, by using a little calculations and applying the algorithm PSO, the solution of the CVP is consequently obtained. Approach efficiency is confirmed by numerical examples.

scholarly journals Path Planning for Unmanned Underwater Vehicle Based on Improved Particle Swarm Optimization Method

2018 ◽  
Vol 14 (12) ◽  
pp. 137
Author(s):  
Jianhua Xu ◽  
Hao Gu ◽  
Hongtao Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Potential Field ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Time Varying ◽  
Slowly Varying Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Underwater Vehicle

Path planning of Unmanned Underwater Vehicle (UUV) is of considerable significance for the underwater navigation, the objective of the path planning is to find an optimal collision-free and the shortest trajectory from the start to the destination. In this paper, a new improved particle swarm optimization (IPSO) was proposed to process the global path planning in a static underwater environment for UUV. Firstly, the path planning principle for UUV was established, in which three cost functions, path length, exclusion potential field between the UUV and obstacle, and attraction potential field between UUV and destination, were considered and developed as an optimization objective. Then, on the basis of analysis traditional particle swarm optimization (PSO), the time-varying acceleration coefficients and slowly varying function were employed to improve performance of PSO, time-varying acceleration coefficients was utilized to balance the local optimum and global optimum, and slowly varying function was introduced into the updating formula of PSO to expand search space and maintain particle diversity. Finally, numerical simulations verify that, the proposed approach can fulfill path planning problems for UUN successfully.

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