A Heuristic Operator-Based Quantum Ant Colony Optimization Algorithm for Robot Path Planning

A Heuristic operator-based Quantum ACO algorithm (HQACO) to solve the global robot path planning problem is presented. The idea of the algorithm is that first an ant colony algorithm based on quantum computing is proposed to plan the optimal collisionfree path. Then a new heuristic operator which balances between population diversity and convergence rate is defined. Heuristic roulette selection strategy is adopted to avoid the search being trapped in local optimum. We redefine the heuristic operator to guide the algorithm converge fast. And the Quantum gate (Q-gate) is used to update the pheromone in ACO in order to increase the diversity of population. Simulation results show that HQACO not only enhances the global search capability, but also more effective than the path planning method in the literature, We demonstrate also that the novel ACO algorithm can find more precise solutions for path planning problems under the complex environment.