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

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

Path finding for the coalition of co-operative agents acting in the environment with destructible obstacles

The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper. Some agents can modify the environment by destructing the obstacles thus allowing the other ones to shorten their paths to the goal. As a result the mutual solution of lower cost, e.g. time to completion, may be acquired. We suggest an original procedure to identify the obstacles for further removal that can be embedded into almost any heuristic search planner (we use Theta*) and evaluate it empirically. Results of the evaluation show that time-to-complete the mission can be decreased up to 9–12 % by utilizing the proposed technique. © Springer Nature Switzerland AG 2018

Path finding for the coalition of co-operative agents acting in the environment with destructible obstacles

The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper. Some agents can modify the environment by destructing the obstacles thus allowing the other ones to shorten their paths to the goal. As a result the mutual solution of lower cost, e.g. time to completion, may be acquired. We suggest an original procedure to identify the obstacles for further removal that can be embedded into almost any heuristic search planner (we use Theta*) and evaluate it empirically. Results of the evaluation show that time-to-complete the mission can be decreased up to 9–12 % by utilizing the proposed technique. © Springer Nature Switzerland AG 2018