Chaotic Bee Swarm Optimization Algorithm for Path Planning of Mobile Robots

This paper is based on swarm intelligence and chaotic dynamics for learning. We address this issue by considering the problem of path planning for mobile robots. Autonomous systems assume intelligent behavior with ability of dealing in complex and changing environments. Path planning problem, which can be studied as an optimization problem, seems to be of high importance for arising of intelligent behavior for different real-world problem domains. In recent years, swarm intelligence has gained increasingly high interest among the researchers from different domains such as commerce, science and engineering. Bees’ warming about their hive is an example of swarm intelligence. It’s particularly fitting apply methods inspired by swarm intelligence to sundry optimization problems, and chiefly if the space to be explored is large and complex. In this paper, we propose a new approach to the problem of path planning for mobile robots based on an improved artificial bee colony optimization combined with chaos. In artificial bee colony optimization, chaos is hybridized to form a chaotic bee swarm optimization, which reasonably combines the population-based evolutionary searching ability of artificial bee colony optimization and chaotic searching behavior. The track of chaotic variable can travel ergodically over the whole search space. In general, the chaotic variable has special characters, i.e., ergodicity, pseudo-randomness and irregularity. Generally, the parameters of the artificial bee colony optimization are the key factors to affect the convergence of the artificial bee colony optimization. In fact, however, it cannot ensure the optimization’s ergodicity entirely in phase space because they are absolutely random in the traditional artificial bee colony optimization. Therefore, this paper provides a new method that introduces chaotic mapping with certainty, ergodicity and the stochastic property into artificial bee colony optimization so as to improve the global convergence. Key-Words: path planning problem, artificial bee colony, chaotic dynamics.