Modified Distributed Bees Algorithm with Adaptive Exploration Parameter for Task Allocation in Swarm of Robots

In this paper, we propose the modified distributed bees algorithm (MDBA) for task allocation in a swarm of robots. In MDBA, a self-adaptive mechanism is proposed to adjust the value of exploration parameter automatically, aiming to maintain the balance of exploration and exploitation. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets’ qualities that are represented as scalar values. Decision-making mechanism is distributed and robots choose their assignments taking into account targets’ qualities and distances. We tested the scalability of the proposed MDBA algorithm in terms of number of robots and number of targets. Control parameters inherent to MDBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.