Aiming at the problems of the basic sine cosine algorithm in solving complex optimization problems,such as low so-lution accuracy,slow convergence speed and inability to jump out of local optimality,a bi-group crisscross sine cosine algorithm is proposed.This paper introduces logistic chaostic mapping in the initialization population phase to make the initial population distri-bution more uniform.Non-linear adjustment of the transformation parameters and improvement of the sine cosine position update for-mula to balance the ability of the algorithm to search globally and develop locally to speed up the solution of the algorithm.The bi-group and merit selection strategies are used to realize the complementary advantages and cooperative coevolution of the sine co-sine population and the crisscross population,and to improve the ability of the algorithm to jump out of the local optimal solution and the convergence speed of the algorithm.The improved algorithm is simulated using 23 benchmark test functions and compared with other intelligent optimization algorithms for analysis,and the results show that the improved algorithm has better optimization performance.
sine cosine algorithmchaostic mapcrisscross optimization algorithmbi-groupcooperative coevolution
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