Optimization Algorithm Based On Dynamic Bionic Mechanism And Its Application To UAV

The widespread application of intelligent optimization algorithms in fields such as space flight,express delivery,and intelligent robots has continuously improved the convergence and search performance requirements of intelligent optimization algorithms.With the increasing complexity of tasks,intelligent optimization algorithms are limited by their own parameters and search formulas,and cannot efficiently complete tasks,or even meet the needs of complex tasks.Therefore,improving the search and convergence performance of optimization algorithms has become a very meaningful research topic.This article first studies hybrid biomimetic optimization algorithms,then studies biomimetic optimization algorithms based on single objective and high-dimensional modeling.Finally,as the task difficulty increases,multi-objective dynamic biomimetic intelligent optimization algorithms are studied.The main research content is as follows:Firstly,in order to address the issues of early maturation and easy local optima in the grey wolf optimization algorithm and the pigeon inspired optimization algorithm,the grey wolf optimization algorithm was simplified and inertia factors were added to improve the search formula of the pigeon inspired optimization algorithm.Based on the improved algorithm,a hybrid optimization algorithm is proposed to update the location formula to balance search and development capabilities.Based on the theory of first-order linear time-varying difference equations,prove the convergence of the algorithm and calculate the complexity of the algorithm according to its optimization process.Consider the problem of single aircraft flight path planning,set objective functions and constraints,apply the algorithm to unmanned aerial vehicle path planning,and verify the effectiveness of the algorithm through simulation.Secondly,for traditional whale optimization algorithms,in order to ensure population diversity,the initialization range of the population is limited.In order to solve highdimensional single objective optimization problems,a dynamic convergence factor and an adaptive weight inertia factor are designed for the number of iterations,and the algorithm search formula is updated.Using the optimal value theory of the algorithm,it is demonstrated that the proposed algorithm can converge to a steady state within a fixed number of iterations.Combined with the 3 kinematics model,the UAV path planning single objective function and constraint conditions are constructed,and the high-dimensional constraint problem is transformed into the optimization problem in the optimal path coordinate system.On this basis,the multi strategy whale optimization algorithm is applied to unmanned aerial vehicle path planning.Verify the effectiveness of the proposed algorithm through simulation.Finally,a Pareto sorting whale optimization algorithm is designed for multi-objective optimization problems.Introducing Pareto sorting and adopting a elimination selection strategy,designing adaptive functions and collaborative factors to update position search formulas,eliminating the impact of “data explosio”.Based on the theory of algorithm convergence,it is proven that the multi-objective Pareto sorting whale optimization algorithm is stable within the number of iterations.Combining the ground to air communication model of unmanned aerial vehicles,construct multi-objective functions and constraints,and apply the algorithm to unmanned aerial vehicle path planning.Verify the effectiveness and practicality of the algorithm through simulation.