On Ant Colony Algorithm for Solving Continuous Optimization Problem

One of the most promising innovations in the area of heuristics is the development of evolutionary algorithms. A valuable and novel proposition in this area is ant algorithms. Researchers examining the behavior of real ants developed algorithms and applied them to many optimization problems. Based on classical ant algorithm, a method for solving optimization problem with continuous parameters using ant colony algorithm is proposed in this paper. In the method, the size of artificial ant colony is determined according to the constrained field of the problem, the amount of the change in objective function is introduced as heuristic factor of the algorithm. The searching region is reduced, moved and modified according to the transition probability dynamically. Our experimental results in continuous optimization problem show that this method has much higher convergence speed and the disadvantage of classical ant colony algorithm of not being suitable for solving continuous optimization problems is overcome.