Multiple UCAVs target assignment via Bloch Quantum-Behaved Pigeon-Inspired Optimization

In this paper, an improved air-to-air multiple Uninhabited Combat Aerial Vehicles (UCAVs) target assignment model is established and an integrated advantage function with three affecting factors, which are angle, velocity and combat capability, is proposed. Then, Bloch Quantum-Behaved Pigeon-Inspired Optimization (BQPIO) algorithm is designed and applied to optimize the integrated advantage function and solve the multiple UCAVs target assignment problem, which combines the quantum chromosome mechanism with the basic Pigeon-Inspired Optimization (PIO). The detailed procedure is also given. Comparative results with basic PIO and Particle Swarm Optimization (PSO) verified the feasibility and effectiveness of our proposed approach.