Abstract
By making use of the characteristics of ergodicity, randomicity and regularity of chaotic variables and information entropy, a novel chaotic small-world algorithm is presented to improve the optimization performance of the simple small-world algorithm. Compared with the corresponding simple small-world algorithm and the modified genetic algorithm approach, the optimization results of selected complex functions indicate that the proposed chaotic small-world algorithm is characterized by a strong search capability and a quick convergence speed. A study of parameter performance of the chaotic small-world algorithm aids in further improvement of its optimization capability. Additionally, the chaotic small-world algorithm is applied to mobile robot path planning, and the global path is optimized by the chaotic small-world algorithm based on a MAKLINK graph. Finally, experimental results verify the validity of the chaotic small-world algorithm for robot path planning.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
