scholarly journals Application of particle swarm optimization for gravity inversion of 2.5-D sedimentary basins using variable density contrast

2016 ◽  
Author(s):  
Kunal Kishore Singh ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Gravity Anomalies ◽  
Sedimentary Basins ◽  
Pso Algorithm ◽  
Density Contrast ◽  
Gravity Inversion ◽  
Western Anatolia ◽  
Swarm Optimization

Abstract. Particle swarm optimization (PSO) is a global optimization technique that works similarly to swarms of birds searching for food. A Matlab code in PSO algorithm is developed to estimate the depth to the bottom of a 2.5-D sedimentary basin and coefficients of regional background from observed gravity anomalies. The density contrast within the source is assumed to be varying parabolically with depth. Initially, the PSO algorithm is applied on synthetic data with and without some Gaussian noise and its validity is tested by calculating the depth of the Gediz Graben, Western Anatolia and Godavari sub-basin, India. The Gediz Graben consists of Neogen sediments and the metamorphic complex forms the basement of the Graben. A thick uninterrupted sequence of Permian-Triassic and partly Jurassic and Cretaceous sediments forms the Godavari sub-basin. The PSO results are better than the results obtained by Marquardt method and the results are well correlated with borehole information.

scholarly journals Application of particle swarm optimization for gravity inversion of 2.5-D sedimentary basins using variable density contrast

2017 ◽  
Vol 6 (1) ◽  
pp. 193-198 ◽  
Author(s):  
Kunal Kishore Singh ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Gravity Anomalies ◽  
Sedimentary Basins ◽  
Pso Algorithm ◽  
Density Contrast ◽  
Gravity Inversion ◽  
Western Anatolia ◽  
Swarm Optimization

Abstract. Particle swarm optimization (PSO) is a global optimization technique that works similarly to swarms of birds searching for food. A MATLAB code in the PSO algorithm has been developed to estimate the depth to the bottom of a 2.5-D sedimentary basin and coefficients of regional background from observed gravity anomalies. The density contrast within the source is assumed to vary parabolically with depth. Initially, the PSO algorithm is applied on synthetic data with and without some Gaussian noise, and its validity is tested by calculating the depth of the Gediz Graben, western Anatolia, and the Godavari sub-basin, India. The Gediz Graben consists of Neogen sediments, and the metamorphic complex forms the basement of the graben. A thick uninterrupted sequence of Permian–Triassic and partly Jurassic and Cretaceous sediments forms the Godavari sub-basin. The PSO results are better correlated with results obtained by the Marquardt method and borehole information.

scholarly journals An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 357 ◽  
Author(s):  
Shu-Kai S. Fan ◽  
Chih-Hung Jen
Keyword(s):  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Searching Strategy ◽  
Wide Range

Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

A Hybrid Particle Swarm Optimization Algorithm for Dynamic Environments

2014 ◽  
Vol 926-930 ◽  
pp. 3338-3341
Author(s):  
Hong Mei Ni ◽  
Zhian Yi ◽  
Jin Yue Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Hybrid Particle Swarm Optimization ◽  
Moving Peaks Benchmark ◽  
Chaos Algorithm

Chaos is a non-linear phenomenon that widely exists in the nature. Due to the ease of implementation and its special ability to avoid being trapped in local optima, chaos has been a novel optimization technique and chaos-based searching algorithms have aroused intense interests. Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in static environments. When the particle swarm optimization (PSO) algorithm is used in dynamic multi-objective problems, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, a hybrid PSO algorithm based on chaos algorithm is brought forward. The hybrid PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the chaos algorithm. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on moving peaks benchmark.

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