In this article, a technique called Meta-Optimization is used to enhance the effectiveness of bio-inspired algorithms that solve antenna array synthesis problems. This technique consists on a second optimization layer that finds the best behavioral parameters for a given algorithm, which allows to achieve better results. Bio-inspired computational methods are useful to solve complex multidimensional problems such as the design of antenna arrays. However, their performance depends heavily on the initial parameters. In this paper, the distances between antenna array elements are calculated in order to reduce electromagnetic interference from undesired sources. The results are compared to previous works, showing an improvement on the performance of bio-inspired optimization algorithms such as Particle Swarm Optimization and Differential Evolution. These results are found to be statistically significant based on the Wilcoxon's rank sum test as compared to these methods using the standard parameters proposed in the literature. Furthermore, graphical representations of the Meta-Optimization process called meta-landscapes are presented, showing the behavior of these algorithms for a range of different parameters, providing the best parameter combinations for each antenna problem.