Neighborhood global learning based flower pollination algorithm and its application to unmanned aerial vehicle path planning

Abstract Flower pollination algorithm (FPA) is a meta-heuristic optimization algorithm that imitates the pollination phenomenon of flowering plants in nature. Due to this algorithm is prone to premature convergence when solving complex optimization problems. So this paper introduces a neighborhood global learning based flower pollination algorithm(NGFPA). Firstly, we analyze the FPA using the constant coefficient differential equation and change the FPA’s global equation. Secondly, we build a neighborhood global learning to enhance population diversity. Finally, the population reconstruction mechanism is added to inhibit the population premature convergence. The convergence of NGFPA is proven using the knowledge of differential equations and stochastic function analysis. We test the performance of NGFPA by optimizing CEC2017. Experiment results show that NGFPA has better performance in comparison with other swarm intelligence algorithms. Furthermore, NGFPA is used to solve the problem of unmanned aerial vehicle (UAV) path planning. Simulation results indicate that NGFPA can obtain smoother paths in different obstacle environments. Therefore, NGFPA is effective and valuable.