Availability and performance optimization of urea decomposition system using genetic algorithm and particle swarm optimization

2021 ◽  
Author(s):  
Monika Saini ◽  
Yashpal Singh Raghav ◽  
Ashish Kumar ◽  
Divya Chandnani
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
And Performance ◽  
Urea Decomposition ◽  
Decomposition System

Improved Particle Swarm Optimization (PSO) for Performance Optimization of Electronic Filter Circuit Designs

2012 ◽  
Vol 229-231 ◽  
pp. 1643-1650
Author(s):  
Chong Woon Kien ◽  
Neoh Siew Chin
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Design Parameters ◽  
Pass Band ◽  
Swarm Optimization ◽  
Pass Filter ◽  
High Pass Filter ◽  
And Performance ◽  
High Pass

This article discusses and analyzes particle swarm optimization (PSO) approach in the design and performance optimization of a 4th-order Sallen Key high pass filter. Three types of particle swarm features are studied: basic PSO, PSO with regrouped particles (PSO-RP) and PSO with diversity embedded regrouped particles (PSO-DRP). PSO-RP and PSO-DRP are proposed to solve the stagnation problem of basic PSO. Based on the developed PSO approaches, LTspice is employed as the circuit simulator for the performance investigation of the designed filter. In this paper, 12 design parameters of the Sallen Key high pass filter are optimized to satisfy the required constraints and specifications on gain, cut-off frequency, and pass band ripples. Overall results show that PSO with diversity embedded regrouped particles improve the conventional search of basic PSO and has managed to achieve the design objectives.

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM

2007 ◽  
Vol 10 (2) ◽  
pp. 172-177
Author(s):  
Azhar W. Hammad ◽  
◽  
Dr. Ban N. Thannoon ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Versus Particle

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

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