Hybrid artificial bee colony and flower pollination algorithm for grid-based optimal pathfinding

Abstract

Pathfinding is essential and necessary for agent movement used in computer games and many other applications. Generally, the pathfinding algorithm searches the feasible shortest path from start to end locations. This task is computationally expensive and consumes large memory, particularly in a large map size. Obstacle avoidance in the game environment increases the complexity to find a new path in the search space. A huge number of algorithms, including heuristic and metaheuristics approaches, have been proposed to overcome the pathfinding problem. Artificial Bee Colony (ABC) is a metaheuristic algorithm that is robust, has fast convergence, high flexibility, and fewer control parameters. However, the best solution founded by the onlooker bee in the presence of constraints is still insufficient and not always satisfactory. A number of variant ABC algorithms have been proposed to achieve the optimal solution. However, it is difficult to simultaneously achieve the optimal solution. Alternatively, Flower Pollination Algorithm (FPA) is one of promising algorithms in optimising problems. The algorithm is easier to implement and faster to reach an optimum solution. Thus, this research proposed Artificial Bee Colony – Flower Pollination Algorithm to solve the pathfinding problem in games, in terms of path cost, computing time, and memory. The result showed that ABC-FPA improved the path cost result by 81.68% and reduced time by 97.84% as compared to the ABC algorithm, which led to a better pathfinding result. This performance indicated that ABC-FPA pathfinding gave better quality pathfinding results.