A Variational Butterfly Algorithm Incorporating the Sine-Cosine Strategy
To address the issues of lowPrecision,susceptibility to local optima,and slow convergence speed in the basic Butterfly Optimization Algorithm(BOA),this paper proposed a variant butterfly algorithm incorporating a sine-cosine strategy.First,the population was initialized using a Bernoulli chaotic map,resulting in a more uniform distribution.Next,adaptive weight coefficients were introduced to improve the speed and precision of global and local position updates.Then,in the local position search phase,the Sine-Cosine Algorithm(SC A)was integrated,with a dynamic switching probability to control the use of SC A,thereby enhancing the algorithm's local search capability.Finally,a Gaussian mutation strategy was employed to mutate the optimal solution,enhancing the algorithm's ability to escape local optima.Simulation experiments on eight benchmark test functions demonstrate that the improved algorithm showed significant competitiveness and better performance compared to other algorithms.
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