A UAV path optimization method based on chaos mapping Pelican optimization algorithm

Abstract

This invention discloses a method for optimizing drone paths based on the Pelican optimization algorithm using chaotic mapping. The method first inputs map model parameters, simulates a mountain environment to create a three-dimensional environmental map, then inputs information about the drone path optimization problem and constructs a multi-objective function. Using the improved Pelican optimization algorithm, the algorithm parameters (population size N and maximum iteration number T) are determined, and a logistic chaotic mapping algorithm is used to generate an initial population and calculate fitness. During the algorithm development phase, as the Pelican approaches prey, a prey generation formula is introduced to generate the prey's location. Adaptive search and Lévy flight strategies are also embedded to update the positions of individuals in the population along different dimensions during the development phase. During the algorithm's local exploration phase, the update formula for prey capture is used to calculate the location of the population's global optimal candidate solution, which is marked as a possible prey location and replaced with the prey location. Finally, the iteration process is repeated until the optimal path is output. This method can quickly plan the optimal and safe path for drones.

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