To address the urgent need for rapid source localization of hazardous chemical leaks, this study proposes a multi-strategy enhanced sparrow search algorithm (ESSA). Three key innovations are implemented. First, an improved logistic mapping initialization method is developed to optimize the initial sparrow population generation, thereby enhancing the diversity of initial solutions. Second, a nonlinear control factor is introduced to balance global exploration and local exploitation during position updates. This mechanism ensures effective exploration of the solution space in later iterations. Third, an adaptive step-size adjustment mechanism enables efficient escape from local optima. Simulation results demonstrate ESSA’s capability to rapidly and accurately identify both the intensity and location of chemical leaks. The proposed method achieves localization accuracy within 1% relative error for both leak intensity and location, significantly outperforming other swarm intelligence algorithms (such as glowworm swarm optimization, grey wolf optimizer, genetic algorithm, etc.). Statistical analysis based on 30 independent runs confirms the algorithm’s robustness, with standard deviation coefficients below 0.021. Compared with the original sparrow search algorithm (SSA), ESSA achieves significant error reduction while maintaining comparable computational efficiency. This study provides an effective method for improving the accuracy and efficiency of leak source localization, offering critical technical support for chemical accident emergency response.

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