Aiming at the defects of poor global performance and tendency to fall into local optima of the traditional differential evolution(DE)algorithm

an adaptive black-winged kite differential evolution(ABKDE)algorithm is proposed.Firstly

chaotic mapping and opposition-based learning strategies are introduced into the initial population to increase the diversity of the initial population

which is conducive to subsequent evolution.Secondly

based on the attack behavior idea of blackwinged kite algorithm(BKA)

the multi-mutation strategy is proposed

and the global random reachability mutation is introduced to enhance the ability of the algorithm to jump out of the local optima.Then

a multi-crossover strategy is constructed by integrating the migration behavior of the BKA to ensure the global performance of the algorithm.At the same time

the control parameters are made adaptive to meet the needs of different evolutionary stages of the algorithm

and the individual boundary crossing mirror reflection wall is set.Finally

diversity of population is measured in early and middle stages.When the population dispersion decreases

the bottom elimination mechanism is activated to increase the population diversity and ensure the effective search of the algorithm.Taking the problems of global path planning in simulation environments of different scales as an example

the results of comparative simulation experiment show that the improved algorithm is feasible and effective for the above problems.