Unmanned Aerial Vehicle Cargo Delivery Assignment via Time-Varying Constriction Pigeon-Inspired Optimization with Memory Retrospection

Unmanned aerial vehicles (UAVs) collaboration is a key technology to UAV cargo delivery in the near future. In this paper, distribution requirement parameters are identified to establish a multi-objective cargo delivery assignment model where a large number of tasks are allocated. To optimize high-dimensional multi-UAV task assignment problem, a time-varying constriction pigeon-inspired optimization with memory retrospection (TCMR-PIO) is proposed. A memory retrospection mechanism is developed to increase the multiplicity of pigeon flock and avoid premature convergence. Meanwhile, a time-varying constraint factor is utilized to provide the improved algorithm with higher accuracy and stability. While maintaining the advantage of high convergence speed, an optimized task assignment scheme can be obtained. Comparative simulation experiments with particle swarm optimization (PSO), pigeon-inspired optimization (PIO), quantum pigeon-inspired optimization (QPIO), adaptive weighted pigeon-inspired optimization (AWPIO) and nonlinear dynamic adaptive inertial weight particle swarm optimization (PSO-DAIW) are carried out, and the performance of the TCMR-PIO algorithm validates its effectiveness and superiority on cargo delivery assignment.