Artificial electric field algorithm (AEFA) is a meta-heuristic optimization technique recently developed that has demonstrated efficacy in scientific research and engineering applications. However, it exhibits limitations such as premature convergence jointly with constrained search capability, particularly in complex optimization scenarios. To rectify these defects, this article proposes an enhanced artificial electric field algorithm (EAEFA) by incorporating a hybrid position updating strategy. To fully utilize all agents in the population, simultaneously improving the exploration and exploitation capabilities of AEFA, EAEFA divides its swarm into two groups. The elite group uses Levy flight for better solution precision, while the non-elite group combines a spiral update approach and the intrinsic update method in AEFA for a more thorough exploration of the search space. This combination effectively balances the exploration and exploitation trade-off to facilitate a robust search. Meanwhile, a stagnation interrupt strategy is employed if EAEFA confronts stagnation or premature convergence. Whereafter, the proposed EAEFA is evaluated over thirteen classical benchmark test functions and CEC2014 benchmark test suits. Numerical results unequivocally demonstrate that the proposed algorithm has exceptional performance surpassing recent variants of AEFA and eight well-known existing meta-heuristic methods. Moreover, EAEFA’s success in solving four real-world engineering design problems showcases its practical applicability.