Ant Colony Optimization (ACO)
is a relatively new meta-heuristic and successful technique in the field of
swarm intelligence. This technique was first introduced by Dorigo and his
colleagues. This technique is used for many applications especially problems
that belong to the combinatorial optimization.
ACO algorithm models represent
the behavior of real ant colonies in establishing the shortest path between
food sources and nests. The ants release pheromone on the ground while walkingfrom their nest to food and then go back to the nest. The ants move according
to the richer amount of pheromones on their path and other ants would be
followed and will tend to choose a shorter path which would have a higher
amount of pheromone. Artificial ants imitate the behavior of real ants, but can
solve much more complicated problem than real ants can.
ACO has been widely applied
to solving various combinatorial optimization problems such as traveling
salesman problem (TSP), job-shop scheduling problem (JSP), vehicle routing
problem (VRP), quadratic assignment problem (QAP), etc. Although ACO has a
powerful capacity to find out solutions to combinatorial optimization problems,
it has the problems of stagnation, premature convergence and the convergence
speed of ACO is always slow. These problems will be more obvious when the
problem size increases.
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