The inverse problem of electrical impedance tomography （EIT） poses significant challenges due to its seriously non-linear， ill-posed and under-determined nature， which can lead to inaccurate image reconstructions. To address this issue， this paper proposes a novel EIT method based on a multi-mechanism dynamic search. First， the original conductivity distribution matrix of the target region obtained by Tikhonov regularization method is used as the input of the multi-mechanism dynamic search algorithm. Then， the candidate solutions are randomly initialized in the search space， and dynamic optimization of the conductivity distribution is performed based on five selection mechanisms corresponding to population migration and mating behavior. The objective function is then used to calculate the fitness of each individual and the candidate solution with the smallest fitness value is regarded as the optimal solution. Subsequently， the optimal solution is used to compensate the original conductivity distribution， yielding the optimal conductivity distribution. Finally， the imaging quality of this method is verified through simulations and experiments. The results show that the proposed method achieves the lowest root mean square error （RMSE） value， ranging between 0.15 and 0.4， and the highest structural similarity index measure （SSIM） value， varying between 0.55 and 0.85. Compared with other methods， namely LBP， NR， Tikhonov regularization， TV and GA methods， the proposed method demonstrates superior image quality and maintains robust performance under the influence of noise， thereby meeting the requirements for accurate image reconstruction.