An improved heuristic mechanism ant colony optimization algorithm for solving path planning

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.