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 well-positioned 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.
