Multi-strategy-based artificial bee colony algorithm for AUV path planning with angle constraints - Xu, Jian; Han, Zhiyong; Yin, Liangang; Yan, Zheping; Yu, Yuyang; Ma, Guangzhi (Ocean engineering, 2024)
This paper focuses on a path planning problem of an autonomous underwater vehicle (AUV) traversing target points at desired angles in an obstacle environment with eddy currents. A tangent-spatial partition method for path planning model construction is developed, which guarantees the direction of arrival in addition to extending the scope of waypoint search. An improved artificial bee colony algorithm integrating with multiple evolutionary strategies (ABC-MES) is proposed. More specifically, a population initialization method that follows the best point set principle is presented to increase population diversity and accelerate convergence. A multi-strategy evolutionary approach is developed in the employed bees phase to enhance population quality and establish a decision support database for subsequent upper confidence bound (UCB) learning. The UCB algorithm is then applied in the onlooker bee stage for assessment and screening of the optimal evolutionary strategy according to the accumulation of prior knowledge and the exploration of new knowledge. Finally, the T-distribution and reversal learning are explored in the scout bee stage to update old nectar sources to prevent the algorithm from falling into the local optimal prematurely. The strong ability of the proposed ABC-MES algorithm to jump out of the local optima is ensured by comparing it with 9 existing algorithms in terms of accuracy and stability with the help of 28 test functions. Simulation results reveal that the path generated by the proposed ABC-MES algorithm has lower overall cost accounting for time efficiency, navigation distance and energy consumption. •Adopting a new tangent space partitioning method to construct a path planning model, expanding the search range of path points and ensuring that underwater unmanned vehicles reach the target point at the expected angle.•An improved artificial bee colony algorithm (ABC-MES) was proposed, which integrates multiple evolutionary strategies during the hiring phase and establishes a population learning database to enhance the algorithm’s optimization ability.•In the follower bee stage of the ABC-MES algorithm, utilizing the data knowledge from the hiring bee stage, the upper bound confidence interval algorithm (UCB) was adopted to select the optimal strategy for evolution, reducing the time complexity of the algorithm.•In the reconnaissance bee stage, a combination of T-distribution and reverse learning strategies is adopted to avoid the ABC-MES algorithm falling into the global optimum.