An Hybrid Learning Approach using Particle Intelligence Dynamics and Bacterial Foraging Behavior for Optimized PID Parameters Evolutionary Computation of Control System Transfer Functions
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 6
Abstract
The foraging behavior of E. Coli is used for optimization problems. This paper is based on a hybrid method that combines particle swarm optimization and bacterial foraging (BF) algorithm for solution of optimization results. We applied this proposed algorithm on different closed loop transfer functions and the performance of the system using time response for the optimum value of PID parameters is studied with incorporating PSO method on mutation, crossover, step sizes, and chemotactic of the bacteria during the foraging. The bacterial foraging particle swarm optimization (BFPSO) algorithm is applied to tune the PID controller of type 2, 3 and 4 systems with consideration of minimum peak overshoot and steady state error objective function. The performance of the time response is evaluated for the designed PID controller as the integral of time weighted squared error. The results illustrate that the proposed approach is more efficient and provides better results as compared to the conventional PSO algorithm.
Authors and Affiliations
Astha Singh, Saifur Rahman
Keywords
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- EP ID EP147465
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