An enhanced artificial bee colony algorithm with self-learning optimization mechanism for multi-objective path planning problem

In recent years, path planning has been one of the most concerned problems in mobile robotics. This study investigates a multi-objective path planning problem focused on minimizing path length and maximizing path safety. Based on the characteristics of this problem, a mathematical model is established, and then an enhanced artificial bee colony algorithm is proposed to solve this problem. In the proposed algorithm, a new hybrid initialization strategy is designed to generate a high-quality initial population. In the employed bee phase, in addition to the crossover and mutation operators, two objective-oriented evolutionary operators are developed. In the onlooker bee phase, two self-learning optimization mechanisms are applied to the non-dominated and dominated individuals, respectively. Specifically, the collaborative-based optimization mechanism is designed to improve the quality of the non-dominated individuals. The dominance-guide optimization mechanism is developed to guide the dominated individuals to learn from the non-dominated ones. In the scout bee phase, a novel individual-restart strategy that considers the useful information of global best solutions is investigated, which increases the proposed algorithm’s exploration ability. Finally, the proposed algorithm is compared with five state-of-the-art algorithms on sixteen instances from four representative environments. Simulation results show that the proposed algorithm achieved average improvements of 2.60% and 90.77% on the hypervolume and inverted generational distance metrics, respectively, compared with the algorithm with the second-best performance. These demonstrate the effectiveness and high performance of the proposed algorithm for solving multi-objective path planning problems in terms of both population diversity and solution quality.