An improved ant colony algorithm in continuous optimization

A modified ant colony algorithm for solving optimization problem with continuous parameters 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 genetic algorithm (GA) operations of crossover and mutation can determine the values of the components in the solution. Our experimental results on the problem of nonlinear programming show that our method has a much higher convergence speed and stability than those of simulated annealing and GA.