Particle swarm optimization of L-probe fed E-shaped patch antenna using single-objective function

2010 ◽  
Vol 52 (3) ◽  
pp. 702-706 ◽  
Author(s):  
Kwok L. Chung ◽  
W. Y. Tam
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Patch Antenna ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Single Objective

Particle Swarm Optimization for Ship Degaussing Coils Calibration

2012 ◽  
Vol 182-183 ◽  
pp. 1446-1451
Author(s):  
Ming Ming Yang ◽  
Da Ming Liu ◽  
Li Ting Lian
Keyword(s):  
Magnetic Field ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Scale Factors ◽  
Single Objective

In this paper, we deal with the problem of the ship degaussing coils optimal calibration by a linearly decreasing weight particle swarm optimization (LDW-PSO). Taking the ship’s magnetic field and its gradient reduction into account, the problem is treated as a multi-objective optimization problem. First a set of scale factors are calculated by LDW-PSO to scale the two kinds of objective function, then the multi-objective optimization problem is transformed to a single objective optimization problem via a set of proper weights, and the problem is solved by LDW-PSO finally. A typical ship degaussing system is applied to test the method’s validity, and the results are good.

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

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 Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves

2020 ◽  
Vol 14 (4) ◽  
pp. 285-311
Author(s):  
Bernd Bassimir ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Upper Bounds ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Stopping Criteria ◽  
Swarm Optimization ◽  
Self Adaptive

Abstract We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better candidate solutions than the already-found best solution. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good candidate solutions can be found much faster than setting upper bounds on the iterations and better solutions compared to applying other solutions from the literature.

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