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The Satin Bowerbird Optimization (SBO) is a recently developed meta-heuristic optimization algorithm which was inspired by the Satin Bowerbirds living in Australia's rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. Therefore, in an effort to enhance global convergence speeds and in order to obtain a better performance, this paper introduces the chaos theory into the SBO optimization process. Various chaotic maps were considered in the proposed Chaotic-SBO (CSBO) method in order to replace the main parameter's greatest step size (), which assists in controlling both exploration and exploitation. The proposed CSBO methods are benchmarked within the CEC2014 test problems. The numerical results show that these novel algorithms improve the chaotic maps, especially in the Tent map, as well as improving the performance of the original Satin Bowerbird Optimization Algorithm. Specifically, the CSBO10(Tent) achieved the best performance. [ABSTRACT FROM AUTHOR]