Flower Pollination Algorithm Combining Lens Imaging and Traction Mutation and Its Application.

Due to defects of relatively slow convergence and being easily trapped in local optimal of flower pollination algorithm (FPA), an enhanced lens learning and traction mutation based flower pollination algorithm (LMFPA) is proposed in this paper. First, LMFPA applies a modified lens learning mechanism to improve the distribution of population. Second, LMFPA uses the observation factor based traction mutation strategy to further accelerate the convergence and increase the probability to jumping out of local optimal. 12 benchmark functions from CEC2013 are selected as testbed to evaluate performance of LMFPA with original FPA algorithm and another 3 improved FPA algorithms, including TMFPA (T-distribution mutation-based flower pollination algorithm), t-GSSA(improved sparrow search algorithm based on adaptive t-distribution and golden sine and its application) and PCSPSO (particle compaction and scheduling based particle swarm optimization). Experimental result shows that LMFPA can achieve superior performance both in convergence speed and accuracy. At last, LMFPA is also used to solve 3D path planning for UAVs. Experimental result illustrates that LMFPA can also find better 3D paths for UAVs. [ABSTRACT FROM AUTHOR]