Abstract
A requirement for new robotic manipulators is the ability to detect and manipulate objects in their environments. Robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain using conventional techniques. Therefore, an efficient technique is required to deal with these types of complex and dynamic systems. Differential Evolution (DE) algorithm is a very powerful optimization technique and has become popular in many fields. Arguably, it is now one of the most predominant stochastic algorithms for real-parameter optimization. However, DE is very sensitive to its control parameters of the mutation operation (F) and crossover operation (CR) in such a way that their fine tuning greatly affect DE performance. Fuzzy Adaptive DE (FADE) algorithm is one of the well known adaptive DE variants that show superiority and reliability in solving different types of optimization