scholarly journals Inversion of Residual Gravity Anomalies using Tuned-PSO Technique

2016 ◽  
Author(s):  
Ravi Roshan ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Gravity Anomalies ◽  
Optimization Method ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Residual Gravity ◽  
Amplitude Coefficient ◽  
Improved Technique

Abstract. Many kinds of particle swarm optimization (PSO) technique are now available and various efforts have been made to solve linear and non linear problems as well as one dimensional and multidimensional problem of geophysical data. Particle swarm optimization is a Meta heuristic optimization method that requires the intelligent guess and suitable selection of controlling parameters (i.e. Inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique Tuned–PSO is an improved technique of PSO, in which effort has been made for choosing the controlling parameters and these parameters have selected after analysing the response of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and also validated using synthetic gravity anomalies over various source geometries. Finally Tuned-PSO is applied over field residual gravity anomalies of two different geological terrains to find out the model parameters namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also shows that the strategy has enhanced the exploration capability of proposed method. Thus Tuned–PSO is an efficient and more robust technique to achieve optimal solution with minimal error.

scholarly journals Inversion of residual gravity anomalies using tuned PSO

2017 ◽  
Vol 6 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Ravi Roshan ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Gravity Anomalies ◽  
Optimization Method ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Residual Gravity ◽  
Amplitude Coefficient ◽  
Improved Technique

Abstract. Many kinds of particle swarm optimization (PSO) techniques are now available and various efforts have been made to solve linear and non-linear problems as well as one-dimensional and multi-dimensional problems of geophysical data. Particle swarm optimization is a metaheuristic optimization method that requires intelligent guesswork and a suitable selection of controlling parameters (i.e. inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique, tuned PSO, is an improved technique of PSO, in which efforts have been made to choose the controlling parameters, and these parameters have been selected after analysing the responses of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and validated using synthetic gravity anomalies over various source geometries. Finally, tuned PSO is applied over field residual gravity anomalies of two different geological terrains to find the model parameters, namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also that the strategy has enhanced the exploration capability of the proposed method. Thus tuned PSO is an efficient and more robust technique to achieve an optimal solution with minimal error.

Gliding Trajectory Optimization Method Based on Particle Swarm Optimization and Direct Shooting Method

2014 ◽  
Vol 615 ◽  
pp. 270-275
Author(s):  
Wen Jing Zhang ◽  
Fen Fen Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Direct Method ◽  
Angle Of Attack ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Direct Shooting ◽  
Direct And Indirect Methods

Glide trajectory optimization of vehicle can greatly improve the performance of missile. As is well-known, methods of trajectory optimization can be divided into direct and indirect methods. Generally, the direct method is convenient and can obtain the optimal solution with higher probability. Based on the direct method, a missile trajectory is optimized by discretizing the control quantity (angle of attack) and transforming the original optimal control problem to a nonlinear programing problem (NLP) in the present paper. The particle swarm optimization algorithm that is easy to implement and has higher convergence rate is utilized to solve the transformed NLP to generate the optimal angle of attack rule. Simulation results show that with the optimal rule, gliding distance of missile is clearly improved compared to the initial one.

scholarly journals Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural Area

2017 ◽  
Vol 7 (4) ◽  
pp. 2125
Author(s):  
Messaoud Garah ◽  
Houcine Oudira ◽  
Lotfi Djouane ◽  
Nazih Hamdiken
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Path Loss ◽  
Optimization Method ◽  
Performance Criteria ◽  
Model Parameters ◽  
Loss Reduction ◽  
Swarm Optimization ◽  
Loss Models ◽  
Propagation Prediction

In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms the other studied models.

scholarly journals Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mu Lin ◽  
Zhao-Huanyu Zhang ◽  
Hongyu Zhou ◽  
Yongtao Shui
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Flight Path Angle ◽  
Gaussian Perturbation

This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.

CHARACTERIZATION OF RECYCLED RUBBER USING PARTICLE SWARM OPTIMIZATION TECHNIQUES

2015 ◽  
Vol 88 (3) ◽  
pp. 343-358 ◽  
Author(s):  
I. Uriarte ◽  
E. Zulueta ◽  
T. Guraya ◽  
M. Arsuaga ◽  
I. Garitaonandia ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Viscoelastic Model ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Maxwell Model ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Compression Tests ◽  
Swarm Optimization ◽  
Recycled Rubber

ABSTRACT A material based on recycled rubber has been developed to use as a protective coating on road barriers with the aim of improving motorcyclists' security against crash impacts. This material is based on grounded rubber from used tires added by extrusion using low-density polyethylene as adhesive. Compression tests have been performed for different densities of the recycled material to fully describe the mechanical characteristics under high strain rates (in the rank 0.057–5.7 s−1), and a constitutive model composed of a hyperelastic Mooney Rivlin part and a viscoelastic part based on the generalized Maxwell model has been taken to characterize this behavior. Hyperelastic parameters have been obtained by means of the least-squares fitting technique, and particle swarm optimization (PSO) has been used to obtain viscoelastic parameters. The PSO algorithm is shown to be a good optimization method, simple, versatile, and consisting of few parameters that accelerate to the optimal solution. Therefore, this article presents a new and efficient approach to obtaining the parameters for the viscoelastic model. The behavior of the experimental material confirms the theoretically obtained results, so the procedure presented in the article is validated successfully.

Research on Engineering Project Time-Cost Optimization Based on Particle Swarm Optimization

2011 ◽  
Vol 55-57 ◽  
pp. 1683-1686
Author(s):  
Ting Wang ◽  
Li Feng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Model ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Time Cost ◽  
Engineering Project ◽  
Swarm Optimization ◽  
Nonlinear Relation

In order to reasonably reduce the cost of project, and reduce the duration of project, the engineering project time–cost must be optimized. The paper concludes the project time - cost optimal solution, by establishing programming model of project time–cost nonlinear relation, and using particle swarm optimization algorithm to achieve progress optimization. And using an example shows that this optimization method is the feasibility and practicability in solving engineering project time–cost of nonlinear optimization.

scholarly journals Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haris Masood ◽  
Amad Zafar ◽  
Muhammad Umair Ali ◽  
Muhammad Attique Khan ◽  
Kashif Iqbal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Correlation Filters ◽  
Swarm Optimization ◽  
Promising Tool ◽  
Particle Swarm Optimization Technique ◽  
Selection Of

In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.

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