Solving Constrained Continuous Optimization Problems with GCACO II

A novel version of Ant Colony Optimization algorithm for solving constrained numerical problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to case of continuous space is implemented within the general frameworks. The stigmergic communication is simulated by considering some direction vectors which are memorized. These vectors are normalized gradient vectors that are calculated using the values of the evaluation function and the corresponding values of object variables. The proposed Gradient based Continuous Ant Colony Optimization (GCACO) method is applied to some benchmark problems. The results obtained from GCACO are well comparable and in some cases superior in terms of accuracy and computational demand to those of other algorithms. Also the experiments illustrate the ability of GCACO for solving different types of constrained problems. Whereas for solving the used test cases in this contribution different algorithms have been proposed in past researches.

کلیدواژه ها:

Ant Colony Optimization ، Continuous Space ، Evolutionary Algorithms ، Meta-heuristic ، Natural Metaphoric Algorithms ، Constraint handling