An Improved Butterfly Optimization Algorithm with Multi-Strategy Integration

Aiming at the problems of traditional butterfly optimization algorithm which is easy to fall into local optimum and slow convergence speed, an improved butterfly optimization algorithm (ICBOA) incorporating multiple strategies is proposed. The initial population of Tent chaotic mapping algorithm is optimized to enhance the population diversity; a nonlinear parameter adjustment mechanism is introduced to balance the global search and local exploitation; Cauchy variation is introduced in the global search stage, and stochastic inertia weights are introduced in the local search stage to improve the search efficiency. The ICBOA algorithm is compared and analyzed with the traditional butterfly optimization algorithm and five other optimization algorithms on nine benchmark test functions, and statistically analyzed by box plots, and the results show that the proposed improved algorithm has higher convergence speed and accuracy, and effectively avoids the problem of falling into the local extremes.