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

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