An efficient grid-based path planning approach using improved artificial bee colony algorithm

Grid-based path planning with large solution spaces, is considered computationally hard because of the computational time required to examine all possible paths. Many algorithms have been developed to solve this problem, one of which is the artificial bee colony (ABC) algorithm, known for its strong search capabilities. In this paper, an improved artificial bee colony algorithm (IABC), designed to achieve a balance between exploitation and exploration by integrating two mechanisms, is proposed. First, a path linearization strategy that eliminates unnecessary corners in the planned path within the grid environment is integrated. Second, a local search strategy is employed to enhance the convergence speed of ABC and improve its ability to find the global optimum solution. To evaluate the performance of IABC, it is first compared with the basic ABC in environments of the same size and demonstrates improvements in the range of 7%–14% in terms of path length. Secondly, the contributions of the two improvement strategies are analyzed through ablation studies. Thirdly, IABC is tested for scalability by running it in environments of varying sizes, achieving improvements in the range of 19%–20%. Fourthly, IABC is compared with the advanced ABC variants, achieving improvements in the range of 2%–32%. Fifthly, IABC is compared with the well-known and recent advanced algorithms, achieving improvements starting from 2%. Finally, IABC is evaluated against the results from recent studies in the literature, showing improvements of up to 37%. These results demonstrate that IABC is an effective method for solving grid-based path planning problems.