Firefly Algorithm and Particle Swarm Optimization for photovoltaic parameters identification based on single model

The current study deals with the optimization of significant parameters of aluminium and copper rectangular porous fins using firefly algorithm with reflective boundary condition. The study has been done considering convective heat transfer, in the first case, as well as combined convective and radiative modes of heat transfer, in the second case. To solve the non-linear governing equation, a semi analytical technique, differential transformation method is adopted. The results obtained by differential transformation method are validated by the numerical solution obtained by the finite difference method. The performance of firefly algorithm is evaluated by comparing with the results obtained by particle swarm optimization where it is seen that for the current set of equations, firefly algorithm took lesser number of iterations and computational time to converge than particle swarm optimization for all the cases. The analysis has been done for three different fin volumes and the effect of important variables which directly influence the heat transfer rate through porous fins has been discussed.

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Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

Journal of Vibration and Control ◽

10.1177/1077546315580693 ◽

2016 ◽

Vol 23 (3) ◽

pp. 501-514 ◽

Cited By ~ 15

Author(s):

Mat Hussin Ab Talib ◽

Intan Zaurah Mat Darus

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Firefly Algorithm ◽

Ride Comfort ◽

Particle Swarm ◽

Mr Damper ◽

Suspension System ◽

System Response ◽

Swarm Optimization ◽

Magneto Rheological

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force–displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL–PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.

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