An Improved Ant Colony Optimization Algorithm

Special Issues Editor (Nottingham Tent University, United Kingdom (Great Britain))

To overcome the faults of slow convergence rate and ease of falling into local optimal solutions in classical Ant Colony Optimization algorithms, an improved algorithm is proposed from three aspects: i) adjustment of the state transition rule, ii) alteration of the pheromone updating rule, and iii) integration of local optimization algorithms. The simulation results show that the proposed algorithm has much higher capacity of searching global optimal solution and faster convergence rate than the classical algorithms.

Journal: International Journal of Simulation: Systems, Science & Technology, IJSSST V17

Published: Jul 14, 2016

DOI: 10.5013/IJSSST.a.17.26.33