<p>Antenna array synthesis is one of the most significant optimization problems in electromagnetics. In addressing the challenge of balancing convergence accuracy and computational efficiency in existing optimization algorithms, this paper introduces a chaos-enhanced adaptive multi-mutation arctic puffin optimization (CEAM-APO) algorithm. The proposed algorithm establishes a three-stage enhancement framework that significantly improves optimization performance in electromagnetic applications, including sidelobe suppression and null control. First, the presented approach integrates the tent chaotic mapping strategy for uniform initialization during the initialization stage, effectively reducing the risk of premature convergence to local optima. Next, a fusion mutation strategy based on t-distribution perturbation with adaptive scaling is used to adjust the mutation probability adaptively during the middle stage, improving both convergence speed and accuracy. Subsequently, a local refinement operator incorporating the golden sine for directed refinement is introduced to update the tail individual in the last stage, enhancing overall solution performance. To verify the performance of this framework, evaluations on four classical benchmark functions demonstrate its performance over the original APO and other mainstream algorithms (PSO, JS, GWO, SA) in both convergence speed and solution accuracy, achieving a 100% success rate. Furthermore, when applied to linear and planar array synthesis, the proposed CEAM-APO method also demonstrates stable and superior results in key performance metrics such as sidelobe suppression and null control.</p>