Some disadvantages refer the multiple parameters used for the algorithm and the high hardware resources requirements. The Ant Colony System algorithm is an example of an Ant Colony Optimization method from the field of Swarm Intelligence, Metaheuristics and Computational Intelligence.The ants solution construction is guided by pheromone trails and problem dependent heuristic information.An individual ant constructs candidate solutions by starting with an empty solution and then iteratively adding solution components until a complete candidate solution is generated.
The most representative meta-heuristics include genetic algorithms, simulated annealing, tabu search and ant colony.
An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. Nevertheless, sub-optimal solutions are sometimes easy to find.
Consequently, there is much interest in approximation and heuristic algorithms that can find near optimal solutions within reasonable running time.
Obviously, some real world problems may have different cluster structures, but the solution procedure presented in this paper is able to handle any cluster structure.
The decimal values can be treated as parameters and can be changed if it is necessary.