A fuzzy grouping-based memetic algorithm for multi-depot multi-UAV power pole inspection

Power pole inspection is important to maintain the normal operation of electrical system. It usually requires to fly a fleet of unmanned aerial vehicles (UAVs) from multiple depots at the same time to jointly complete the inspection tasks distributed in a wide area, which is a challenging planning problem. In order to address this problem, this work first builds the model of the multi-depot multi-UAV power pole inspection problem with charging stations. After that, a fuzzy grouping-based memetic algorithm named FGATS is proposed to solve the problem. Specifically, a fuzzy grouping strategy is proposed to divide a large-scale problem into multiple small-scale subproblems in order to reduce the complexity of the problem. It continuously adjusts the grouping scheme to enhance the flexibility and effectiveness of the algorithm. Then, a hybrid algorithm combining genetic algorithm and tabu search is designed to jointly optimize the subproblems, ensuring an effective balance between global and local searches. After a certain number of iterations, the problem is re-divided and the populations are re-initialized by the proposed solution update strategy that learns and incorporates historical task-sequence knowledge. This strategy enhances the current optimization process by retaining useful information from previous iterations. Experiments on both artificial terrains and a real terrain verifies the effectiveness of FGATS, with the algorithm’s performance ranking first overall.