Improved black-winged kite algorithm based on chaotic mapping and adversarial learning

Abstract Aiming at the shortcomings of the black-winged kite algorithm, such as poor uniformity of initial population distribution, subjective search and single direction, this paper proposes an improved black-winged kite algorithm based on chaotic mapping and adversarial learning. It then progresses to adopting Tent chaotic mapping to achieve a uniform initial solution distribution, introducing Beta random distribution and optimizing the nonlinear factor in the attack phase to make the search trend more in line with the demand, and introducing an adversarial learning mechanism to extend the search direction. Twelve benchmark test functions are tested and compared with five other algorithms, and the result shows that the algorithm significantly outperforms the other algorithms in terms of searching ability and optimization-finding accuracy.