Parameter Optimization and Solution Performance Analysis of Multi-Modal Butterfly Optimization Algorithm

Traditional optimization algorithms often have the problems of slow convergence and poor accuracy when facing complex situations. Therefore, a parameter optimization method based on multi-modal butterfly optimization algorithm is proposed for parameter adjustment of support vector machine and infinite impulse response digital filter. By introducing storage mechanism strategy, the accelerated detection mechanism and purification program strategy, the global searching ability and solving efficiency of the algorithm are improved. The results showed that the improved multi-modal butterfly optimization algorithm reached its optimal solution in the fifth iteration, and the objective function value reached its minimum without changing. The precision and recall of this algorithm were above 0.9, and the variance was close to 0. This algorithm successfully solved 942 epochs with a success rate of 93.314%. The total solution time was 14.338 seconds, and the average solution time was 0.014 seconds. The improved multi-modal butterfly optimization algorithm showed excellent performance in optimizing parameters, effectively improving parameter optimization performance, and showed high efficiency, stability, and reliability. This study is helpful to promote optimization algorithms and provide more efficient and reliable methods for solving practical problems.