A reinforcement learning based artificial bee colony algorithm with application in robot path planning. (1st October 2022) - Cui, Yibing; Hu, Wei; Rahmani, Ahmed
Abstract: Artificial bee colony (ABC) algorithm is a popular optimization algorithm with excellent exploration ability and various applications. Nevertheless, its effectiveness is limited by the one-dimensional search strategy. Therefore, in order to improve its performance, a reinforcement learning (RL) based ABC algorithm is proposed (named ABC_RL). In ABC_RL, the number of dimensions to be updated in search equation of the employed bee phase is varied and adjusted intelligently via RL. Moreover, two improved search strategies are adopted to maintain a nice balance between diversification and intensification. The performance of ABC_RL is evaluated through a series of comparisons conducted on CEC 2017 benchmark problems. The results indicate that ABC_RL outperforms the compared ABC variants considering the solution accuracy. In addition, a robot path planning problem is concerned to further test the effectiveness of ABC_RL. And the comparison results show the advantages of ABC_RL in terms of path length and running time. Highlights: A reinforcement learning based artificial bee colony algorithm is proposed. The frequency of perturbation is adjusted intelligently. Two enhanced solution search equations are developed. A type of heavy-tailed distribution is used to improve randomness. Experiments and application show nice performance of the proposed ABC_RL algorithm.