A black-winged kite optimization algorithm enhanced by osprey optimization and vertical and horizontal crossover improvement.

This paper addresses issues of inadequate accuracy and inconsistency between global search efficacy and local development capability in the black-winged kite algorithm for practical problem-solving by proposing a black-winged kite optimization algorithm that integrates the Osprey optimization algorithm and Crossbar enhancement (DKCBKA). Firstly, the adaptive index factor and the fusion Osprey Optimization Algorithm approach are incorporated to enhance the algorithm's convergence rate, and the probability distribution factor is updated throughout the attack stage. Second, the stochastic difference variant method is implemented to prevent the method from entering the local optima. Lastly, the longitudinal and transversal crossover technique is incorporated to enhance the algorithm's convergence accuracy and to dynamically alter the population's global and individual optimal solutions. Fifteen benchmark functions are chosen to test the effectiveness of the enhanced algorithm and to compare the optimization efficiency of each technique. Simulation experiments are performed on the CEC2017 and CEC2019 test sets, revealing that the DKCBKA algorithm surpasses five standard swarm intelligence optimization methods and six improved optimization algorithms regarding solution accuracy and convergence speed. The superiority in meeting real optimization challenges is further demonstrated by the optimization of three real engineering optimization problems by DKCBKA, with optimization capabilities 18.222%, 99.885% and 0.561% higher than BKA, respectively.