A Method for Solving Optimization Problem in Continuous Space Using Ant Colony Algorithm
蚁群算法进行连续参数优化的新途径

A method for solving optimization problem with continuous parameters using 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 the values of the components in the solution can be determined by the operations of cross and mutation. Our experimental results of the problems 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..