An effective TLBO-based memetic algorithm for hypersonic reentry trajectory optimization

Abstract

In this paper, an effective Teaching-Learning Based Optimization (TLBO)-based memetic algorithm (TLBO-MA) is proposed to enhance the searching quality and efficiency of conventional TLBO, as its global fast coarse search capability and risks of getting prematurely stuck in local optima for the numerical optimization problems. In the proposed TLBO-MA, both TLBO-based operator and some special local searching operators are designed to balance the global exploration and local exploitation abilities. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of the memetic algorithm. To decide, at runtime, which local method is chosen, we adopt adaptive Meta-Lamarckian learning strategy. Finally, experimental studies with adaptive Meta-Lamarckian learning strategy on continuous benchmark problems and hypersonic trajectory optimization problem are presented. Simulation results on six benchmark problems and comparisons with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and conventional TLBO indicate that the proposed TLBO-MA can not only effectively enhance the searching efficiency, but also greatly improve the searching quality. Simulation results on trajectory optimization demonstrate the feasibility of the proposed TLBO-MA to actual engineering problem.