A novel small-world algorithm incorporating chaos optimization and its application to robot path planning

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.