Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization

2015 ◽  
Vol 8 (1) ◽  
pp. 309-331 ◽  
Author(s):  
S. Mallick ◽  
R. Kar ◽  
D. Mandal ◽  
S. P. Ghoshal
Keyword(s):  
Particle Swarm Optimization ◽  
Analog Circuits ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Sizing ◽  
Cmos Analog Circuits ◽  
Gravitational Search

scholarly journals Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sahazati Md Rozali ◽  
Mohd Fua’ad Rahmat ◽  
Abdul Rashid Husain
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Tracking Error ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Backstepping Controller ◽  
Gravitational Search

This paper presents backstepping controller design for tracking purpose of nonlinear system. Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization (PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. The performance is evaluated based on the tracking error between reference input given to the system and the system output. Then, the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance. The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error.

Optimization of constructal T-shaped porous fins under convective environment using Swarm Intelligence Algorithms

2020 ◽  
Vol 234 (14) ◽  
pp. 2100-2113
Author(s):  
Tuhin Deshamukhya ◽  
Dipankar Bhanja ◽  
Sujit Nath
Keyword(s):  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Computational Effort ◽  
Swarm Optimization ◽  
Optimum Point ◽  
Gravitational Search ◽  
Porous Fins

Constructal T-shaped porous fins transfer better heat compared to the rectangular counterparts by improving the heat flow through the low resistive links. This type of fins can be used in aerospace engines which demand faster removal of heat without adding extra weight of the overall assembly. Here, in this study, three powerful nature-inspired metaheuristic algorithms such as particle swarm optimization, gravitational search algorithm, and Firefly algorithm have been used to optimize the dominant thermo physical as well as geometric parameters which are responsible for transferring heat at faster rates from the fin body satisfying a volume constraint. The temperature distribution along the stem and the flange has been plotted, and the effect of important parameters on the efficiency has been determined. Three different volumes are selected for the analysis, and the results have shown marked improvement in the optimized heat transfer rate. Particle swarm optimization has reported an increase of 0.81%, while Firefly algorithm reports 0.83% improvement as we increase the fin volume from 500 to 1000 and 0.19% (by PSO) and 0.4% (by FA) as the volume increases from 1000 to 1500. The paper also presents a scheme of reducing the computational effort required by the algorithms to converge around the optimum point. While a reduction of 14.36% computational effort has been achieved in particle swarm optimization’s convergence time, Firefly algorithm took 24.64% less time to converge at the near-optimum point. While particle swarm optimization has converged at better optimal points compared to Firefly algorithm and Gravitational search algorithm, Gravitational search algorithm has outperformed the two algorithms in terms of computational time. Gravitational search algorithm took 61.72 and 29.33% less time to converge as compared to particle swarm optimization and Firefly algorithm, respectively.

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