The Design of Enhanced Black-winged Kite Optimization Algorithm Based on Multi-Strategy Improvement

To address the limitations that the fundamental black-winged kite optimization algorithm tends to drop into local optimum and the optimization accuracy is insufficient., a multi-strategy modified black-winged kite optimization algorithm (MEBKA) incorporating chaotic mapping and dynamic inertia weights is proposed. Firstly., chaotic mapping is employed for initialization which enriches the swarm diversity and avoids falling into local optimum; secondly., a dynamic inertia weighting mechanism is introduced to animate the algorithm's accuracy and convergence speed. Finally., to confirm the validity of the algorithm., the study uses the benchmark test function for evaluation and conducts comparison experiments with five classical algorithms., including the Gray Wolf Optimization Algorithm (GWO)., Frost and Ice Optimization Algorithm (RIME)., Whale Optimization Algorithm (WOA)., Black-winged Kite Algorithm (BKA)., and Particle Swarm Algorithm (PSO). Experimental results indicate that the MEBKA algorithm has best convergence performance., solution precision., and stability.