Enhanced Teaching–Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem

Abstract

This research proposes an enhanced teaching–learning based optimization (ETLBO) algorithm to realize an efficient path planning for a mobile robot. Four strategies are introduced to accelerate the teaching–learning based optimization (TLBO) algorithm and optimize the final path. Firstly, a divide-and-conquer design, coupled with the Dijkstra method, is developed to realize the problem transformation so as to pave the way for algorithm deployment. Secondly, the interpolation method is utilized to smooth the traveling route as well as to reduce the problem dimensionality. Thirdly, an opposition-based learning strategy is embedded into the algorithm initialization to create initial solutions with high qualities. Finally, a novel, individual update method is established by hybridizing the TLBO algorithm with differential evolution (DE). Simulations on benchmark functions and MRPP problems are conducted, and the proposed ELTBO is compared with some state-of-the-art algorithms. The results show that, in most cases, the ELTBO algorithm performs better than other algorithms in both optimality and efficiency.

Keywords

• optimization
• metaheuristic
• path planning
• divide-and-conquer
• teaching–learning based optimization algorithm
• differential evolution