The sine cosine algorithm(SCA) is a new population-based stochastic optimization method. It uses sine and cosine functions to fluctuate the solution run to the global optimal solution. Its linear adjustment strategy and weak local search ability seriously affect the performance of the algorithm. In order to improve the calculation accuracy of the sine cosine algorithm, an alternating sine cosine algorithm based on the elite chaotic search strategy is proposed, which uses the nonlinear adjustment strategy based on logarithmic curve to modify the control parameters, uses the elite individuals' chaotic search strategy to enhance the exploitation ability of the algorithm. The SCA based on this strategy and the opposition-based learning algorithm are alternately implemented to enhance the exploration ability, reduce the time complexity and improve the convergence speed of the algorithm. The proposed method has been tested by 23 benchmark test functions, and compared with the improved SCA and the state-of-the-art heuristic algorithm. The comprehensive parameter experiment and results analysis show the effectiveness and superiority of the proposed algorithm.