Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller

Weed is a phenomenon which is looks for optimality and finds the best environment for life and quickly adapts itself to environmental conditions and resists changes. Considering these features, a powerful optimization algorithm is developed in this study. The invasive weed optimization algorithm (IWO) is a population-based evolutionary optimization method inspired by the behavior of weed colonies. In this paper, the IWO algorithm is based on chaos theory. Among parameters of weed optimization algorithm, standard deviation affects the performance of the algorithm significantly. Therefore, chaotic maps are used in the standard deviation parameter. Performance of the chaotic invasive weed development method is investigated on five benchmark functions, using logistic chaotic mapping. Additionally, the problem of setting the PID controller parameters for a DC motor using the proposed method is discussed. The statistical results on optimization problems show that the improved chaotic weed algorithm has gained fast convergence rate and high accuracy.