Abstract:
Ant Colony Optimization (ACO) is a class of
heuristic search algorithms that have been
successfully applied to solving combinational
optimization (CO) problems. The traveling salesman
problem (TSP) is among the most important
combinatorial problems. ACO has very good search
capability for optimization problems. But it still has
some drawbacks such as stagnation behavior, long
computational time, and premature convergence
problem of the basic ACO algorithm on TSP. Those
problems will be more obvious when the complexities
of the considered problems increase. The proposed
system based on basic ACO algorithm based on wellpositioned
the ants on the initiation and information
entropy which is applied to tuning of the algorithm’s
parameters. Then, ACO for TSP has been improved
by incorporating local optimization heuristic.
Therefore, the proposed system intends to reach
superior search performance over traditional ACO
algorithms do.
