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The effective selection of Variable Cycle Engine (VCE) parameters plays a key role in achieving low specific fuel consumption (SFC) of fighters. However, the selection of VCE parameters is a continuous multimodal issue involving substantial local optima, so that most swarm intelligence (SI) algorithms are easily trapped into local optimal solutions, and cannot obtain satisfactory performance. To address this problem, an improved moth flame optimization algorithm with adaptive Lévy-Flight perturbations (ALFMFO) is proposed. In ALFMFO, the current population aggregation status can be accurately judged based on the difference in fitness variance between two successive moth generations. According to the population aggregation status, the Lévy-Flight disturbance strategy can adaptively adjust the perturbation probability to enhance the ability of ALFMFO to escape from local optimal solutions and realize the minimum SFC optimization of VCE. Experimental results suggest that ALFMFO is effective and superior to other compared SI algorithms in terms of accuracy and robustness.