Chaos-Optimized Adaptive Glowworm Swarm Optimization for Indoor Robot Path Planning
To address the general problems of biological intelligence algorithms such as the standard Glowworm Swarm Optimization(GSO)algorithm for global path planning,which tends to fall into local optimality,slow convergence and long search paths,a new method for robot path planning,the Chaos-optimized Adaptive Glowworm Swarm Optimization(CAG-SO)algorithm,is proposed.In this method,firstly,the initial position of the firefly is ini-tialized using a chaotic sequence generated by cubic mapping to improve the global search ca-pability of global path planning;secondly,changing the firefly search step size,the algo-rithm's operation speed and search accuracy are enhanced based on the chaotic optimization technique.Finally,by simulating the complex and changeable working environment in the laboratory on MATLAB,the CAGSO algorithm,GSO algorithm and Particle Swarm Opti-mization(PSO)algorithm are compared and verified and the algorithm performance is ana-lyzed.The experimental results show that the improved algorithm shortens the length of the global path,reduces the convergence time and solves the problem that the standard glow-worm swarm optimization algorithm tends to get trapped in local optima.
万方数据
维普
