Chaotic satin bowerbird optimization for improving the efficiency of numerical function optimization

Abstract

The Satin Bowerbird Optimization (SBO) is a recently developed meta-heuristic optimization algorithm, 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 to obtain 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. We tested CSBO algorithms through experiments the numerical function optimization. The numerical results indicate that the CSBO algorithm outperformed 11 other optimization algorithms.