Focused on the issue that the Sine Cosine Algorithm is trapped in the local minima due to premature convergence in complex nonlinear optimization problems, an improved Sine Cosine Algorithm based on Levy flight was proposed. The algorithm combines the current rank of individual fitness and its historical fitness value to mark the individuals which may fall into local minimums. The marked individuals update their position by using the variable parameters Levy flight, which enhances the algorithm's global searching ability in the exploration period and the local searching ability in the exploitation period. Five benchmark functions are used to test the performance of the algorithm. The theoretical analysis and simulation results show that the proposed algorithm performs well in the complex nonlinear optimization problem.
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