An adaptive gradient descent-based local search in memetic algorithm applied to optimal controller design

Abstract

Memetic Algorithm (MA) is a combination of Evolutionary Algorithms (EAs) and Local Search (LS) operators known as hybrid algorithms. In this paper, an efficient MA with a novel LS, namely Memetic Algorithm with Adaptive LS (MA-ALS), is proposed to improve accuracy and convergence speed simultaneously. In the core of the proposed MA-ALS, an adaptive mechanism is carried out in LS level based on the employment of specific group with particular properties, which is inspired from an elite selection process. Thus, the proposed adaptive LS can help MA to execute a robust local refinement. This methodology reduces computational costs without loss of accuracy. The algorithm is tested against a suite of well-known benchmark functions and the results are compared to GA and the two types of MAs. A permanent DC motor, a Duffing nonlinear chaotic system and a robot manipulator with 6 degree-of-freedom are employed to evaluate the performance of the proposed algorithm in optimal controller design. Simulation results demonstrate the feasibility of the algorithm in terms of accuracy and convergence speed.