Swarm algorithms are an efficient means of optimization. They work by leveraging a population of solutions that move toward one of the best solutions using different equations based on the different natural mechanisms adopted in the algorithms. Chaos theory has been studied by numerous researchers to enhance the optimization prowess of different swarm algorithms. This work explores the optimization of the Artificial Protozoa Optimizer, a recently discovered swarm technique, using 10 different chaotic functions. The efficiency of the suggested approach is assessed in several dimensions and with different optimization functions. The outcomes validate the effectiveness of the chaotic maps to improve the potential of Artificial Protozoa Optimizer as a reliable optimization method.