Global and Local Moth-flame Optimization Algorithm for UAV Formation Path Planning Under Multi-constraints

To improve the global and local search ability of moth-flame optimization algorithm, three optimization strategies are proposed in this paper, namely chaos-based moth initialization, adaptive weighted position update strategy and population diversity improvement strategy. In moth initialization process, chaos-based Logistic map is adopted to improve population diversity. Then, a nonlinear weighting factor is introduced into the spiral function to adaptively balance the global and local search ability. Besides, new moth is generated by population diversity improvement strategy, which improves diversity and optimality of the population. Finally, simulation tests of unmanned aerial vehicle (UAV) formation under multi-constraints are carried out and comparison results show that the proposed global and local moth-flame optimization algorithm has the superiority in rapidity and optimality in UAV path planning problem compared with the latest path planning algorithms.