Heterogeneous multi-UAVs cooperative task allocation based on the improved coati optimization algorithm

This paper addresses the complex problem of heterogeneous multi-unmanned aerial vehicle (UAV) cooperative task allocation, which inherently possesses multiple potential solutions. A novel cooperative task allocation model that incorporates two critical operational factors UAV dynamic velocity adaptation and mission time cost is established with minimizing both total flight distance and mission completion time. To address the problem posed by mixed variables, an improved coati optimization algorithm (ICOA) is proposed. The enhancement framework comprises three principal components: Firstly, an initialization strategy integrating circle mapping and reverse learning is implemented to generate high-quality initial solutions. Secondly, the Archimedean spiral-guided search mechanism combined with the adaptive mutation operator and exponentially decaying factor is developed to effectively prevent premature convergence and to dynamically balance exploration-exploitation trade-offs. Comprehensive comparative experiments were conducted against three metaheuristics algorithm: the coati optimization algorithm (COA), adaptive genetic algorithm (AGA), and dynamic weight particle swarm optimization (DWPSO) to validate the optimization capabilities of the proposed ICOA. The evaluation framework incorporated three simulation scenarios specifically designed for cooperative task allocation problems.