# 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. optimization; metaheuristic; path planning; divide-and-conquer; teaching-learning based optimization algorithm; differential evolution +
Footnote †: journal: Applied Sciences  
local optimum, low search efficiency, and large burden of computation [8; 9; 10]. Therefore, this work focuses on designing a more efficient procedure and methodology to generate solutions for MRPP problems. In real-world applications, major efforts have concentrated on strengthening by hybridizing one candidate bionic intelligent optimization algorithm with other sophisticated skills. The ACO is one example: some of the most recent ACO-based path-planning algorithms are reviewed as follows. To reduce search blindness, [PERSON] et al. [11] embedded the directional, heuristic information and unevenly distributed initial pheromone into the ACO architecture. The rollback strategy was introduced by [PERSON] et al. [12] to make more ants reach the goal, and the death strategy was also utilized to reduce the influence of an invalid pheromone on the algorithm evolution. [PERSON] et al. [13] designed an improved, pseudo-random proportional rule for the ant state transition which greatly improved the convergence speed. A novel penalty strategy to volatilize the poor path pheromone was embedded into the ACO by [PERSON] et al. [14], which efficiently improved the utilization rate of the ant exploration. [PERSON] et al. [15] tried to improve the visibility of heuristic information by virtue of the principle of infinite step length. In addition, the pheromone update rules, as well as the evaporation rate, were dynamically adjusted to accelerate the convergence speed. The evaluation function of the A-star algorithm and the curvature suppression operator were introduced into the ACO by [PERSON] et al. [16]; these additions were in favor of accelerating the convergence and improving the smoothness of the final path. [PERSON] et al. [17] proposed an attenuation-factor-based heuristic information mechanism, and adjusted the pseudo-random ratio of state selection by means of an adaptive function. The adaptive adjustment factor and pheromone volatilization factor were embedded into the pheromone update rule by [PERSON] et al. [18]. Such modifications efficiently balanced the global search ability and convergence. As can be noted from the above literature, different skills have been embedded into the basic algorithm with respect to the aspects of path formulation, path adjustment, solution modification, individual update, etc. The proposed ETLBO is motivated and designed by these observations and is developed by virtue of TLBO. Many practical applications have proven TLBO to be superior to some conventional metaheuristics [19; 20; 21]. TLBO simulates the behaviors of the teacher and students in the classroom, where the so-called teacher and learner phases are utilized to update the population in the algorithm evolution.