A new method for multi-objective optimization problem based on multi-ant-colony algorithm

In order to improve the solving performance of multi-objective optimization problem, a new method based on multi-ant-colony algorithms is proposed. Aiming to enhance the diversity of pareto solutions, quasi-pareto solutions are constructed by sub-ant-colony algorithm which adopts its own and other sub-ant-colony heuristic information and quasi-pareto solutions obtained by every ant are used for control judgment. The constructed farther-group ants with the quasi-pareto solutions which act as space nodes constitute TSP(Traveling Salesman Problem), and then the solutions of the TSP act as the front of solutions for multi-objective optimization problem, hence lead to the enhancement of the uniform distribution of pareto solutions. Experiment results show that the obtained pareto solutions by multi-ant-colony optimization based on multi-classification methods have many advantages, such as the diversity and uniform distribution of solutions.