Path Planning for Robot Based on IACO-SFLA Hybrid Algorithm

Abstract

In order to solve the problem of slow convergence, low efficiency and easily getting into local optimal solution in robot path planning which is based on basic ant colony algorithm, a new algorithm combining improved ant colony algorithm (IACO) with shuffled frog leaping algorithm (SFLA) is proposed. In the novel ant colony algorithm, dynamic state transition strategy, dynamic global pheromone updating strategy and dynamic pheromone evaporation factor updating strategy are used to ensure the accuracy of solution and the convergence speed of the algorithm. Furthermore, conditional fallback strategy is adopted to ensure the global search ability of the novel algorithm. Numerical experiments show that the optimal path can be more effectively generated by using the improved leapfrog algorithm and the simplified operator in the last step.