A Deadlock-Free Hybrid Estimation of Distribution Algorithm for Cooperative Multi-UAV Task Assignment with Temporally Coupled Constraints

This article addresses the cooperative multiunmanned aerial vehicles task assignment problem (CMTAP) with temporally coupled constraints and aims to find a feasible assignment to minimize the equivalent distances of all tasks. We first present a mixed-integer linear programming model of CMTAP. To solve the undesirable deadlocks of CMTAP, a Petri net amender is constructed based on a candidate solution, and a deadlock-free solution is equivalent to a feasible transition sequence that can be fired sequentially in the corresponding amender. With this amender, we present a Petri net-based deadlock amending method (PDAM) with polynomial time complexity to convert a deadlocked solution into a deadlock-free solution. Also, a deadlock-free hybrid estimation of distribution algorithm (DHEDA) is developed for CMTAP by embedding PDAM into the original EDA. To further improve the solution quality, we establish a local exploitation method, and an adaptive operational probability is used to balance the computational burden and local exploitation ability. Then, a match-up-based reassignment method is proposed to cope with time-sensitive targets. Finally, extensive computational experiments demonstrate that PDAM is more effective at solving deadlocks than graph-based methods, particularly for large-scale CMTAP, and DHEDA outperforms existing algorithms when solving CMTAP.