<p>This paper presents the Aurora Optimizer, a novel optimization algorithm inspired by the dynamic interactions of charged particles with magnetic fields, as observed in the natural Aurora phenomenon. Two innovative strategies based on the selection of the best-found solutions are proposed to guide the movement of search agents within the search space. These strategies effectively balance exploration and exploitation, thereby optimizing both global search capability and local solution refinement. The performance of the Aurora Optimizer is extensively evaluated using three distinct datasets: a suite of 23 benchmark functions, a larger set comprising 50 benchmark functions, and the IEEE CEC2022 benchmark suite. Experimental findings demonstrate that the Aurora Optimizer consistently outperforms several state-of-the-art algorithms in terms of accuracy in reaching the optimal solutions and convergence speed. Additionally, the practical effectiveness of the Aurora Optimizer is validated through its successful application to several complex real-world engineering design problems, including welded beam design, pressure vessel design, spring design, speed reducer design, cantilever beam design, I-beam design, and optimization of a three-bar truss system.</p>