A method for solving optimization problem in continuous space using improved ant colony algorithm

A method for solving optimization problem with continuous parameters using improved ant colony algorithm is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then, crossover and mutation can determine the values of the components in the solution. Our experimental results of the problem of nonlinear programming show that our method has much higher convergence speed and stability than that of GA, and the drawback of ant colony algorithm of not being suitable for solving continuous optimization problems is overcome.