Adaptive chaotic satin bowerbird optimisation algorithm for numerical function optimisation

ABSTRACT

The Satin Bowerbird Optimisation (SBO) 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. To overcome these deficiencies, we propose herein the Adaptive Chaotic Satin Bowerbird Optimisation algorithm (AC-SBO). Within the AC-SBO algorithm, a chaotic map is introduced to modify the search process, with which to enhance global convergence speeds, and to obtain better performance. We introduced the chaos theory into the SBO optimisation process, in order to replace the main parameter’s greatest step size ($\alpha$), which assists in controlling the balance between both exploration and exploitation. The search accuracy and performance of the AC-SBO algorithm were verified on ten classical benchmark functions. In addition, in the experimental CEC2014 results showed that for almost all functions, the AC-SBO technique proved superior to the other comparative algorithms optimisations. The Wilcoxon rank-sum statistical test was performed in order to judge the significance of the results, and further demonstrated the improved performance of the proposed AC-SBO algorithm.

KEYWORDS:

• Satin Bowerbird Optimisation
• meta-heuristic algorithm
• adaptive operator selection
• chaotic map

Disclosure statement

The authors are grateful to the anonymous reviewers and the editor for their suggestions in improving the quality of the paper.