Differential Evolution With Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers

Abstract

This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. In this case, the mutation parameter is dynamically moved during the evolution process by using a shadowed and general type-2 fuzzy systems. The main idea of this work is to make a performance comparison between using shadowed and general type 2 fuzzy systems as controllers of the mutation parameter in differential evolution. The performance is compared with the problem of optimizing fuzzy controllers for a D.C. Motor. Simulation results show that general type-2 fuzzy systems are better when higher levels of noise are considered in the controller.

Overall Dynamic Optimization of Metal Mine Technical Indicators Considering Spatial Distribution of Ore Grade Using AADE

“…It has been well documented that standard optimization methods cannot easily solve complex nonlinear optimization problems [8][9][10]. Many intelligent evolutionary methods have been proposed to solve complex nonlinear optimization problems, such as the genetic algorithm (GA) [11], the particle swarm algorithm (PS) [12,13] and the differential evolution algorithm (DE) [14,15]. DE is an easyto-use algorithm that has few control parameters, is low in computational complexity, and shows good convergence.…”