Архив статей журнала
In order to solve the problems of slow convergence speed and premature convergence at local minima in the traditional butterfly optimization algorithm (BOA), this paper proposes a butterfly optimization algorithm (ITBOA) based on chaos mapping improvement and adaptive distribution, to speed up the optimization process and improve global search capabilities. Chaos mapping improvement is used to generate more diverse population initial values, and adaptive T-distribution adjusts the search strategy according to the current population status. Experimental results show that ITBOA can quickly find the optimal solution under standard benchmark function tests. Compared with the original butterfly algorithm, the butterfly algorithm introducing chaotic mapping (IBOA) and the particle swarm optimization algorithm (PSO), the ITBOA algorithm has faster convergence speed and better search effect.