Adjustable Fully Adaptive Cross-Entropy Algorithms for Task Assignment of Multi-UAVs

This paper investigates the multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment problem. Specifically, we assign different types of UAVs to accomplish classification, attack, and verification tasks targets under resource, precedence, timing constraints. Due complex coupling among these tasks, decompose considered problem into two subproblems: one with continuous independent another correlative tasks. To solve them, first present an adjustable, fully adaptive cross-entropy (AFACE) algorithm based on (CE) method, which serves as a stepping stone for developing other algorithms. Secondly, overcome precedence in subproblem, propose mutually AFACE (MIAFACE) algorithm, converges faster than CE method when obtaining optimal scheme vectors Thirdly, deal second (MCAFACE) find while its computational complexity is inferior that MIAFACE algorithm. Finally, numerical simulations demonstrate proposed (MCAFACE, respectively) consumes less time existing algorithms (correlative,