Abstract:

A new algorithm based on ant colony algorithms and genetic algorithms called Multi-Objective Ant-Genetic Algorithm, which is used to solve the multi-o bjective optimization problem constrained by some conditions, is presented in this paper. Firstly, the solution space is divided into some subspaces, and all the subspaces are labeled by pheromone, then the pheromone guides the inheritance searching and updates itself. Meanwhile, the strategy of updatin g the Pareto optimal decisions and the scheme of converging and exiting the searching are used to promote the efficiency and reduce the complexity of the algorithm. In the end, an example is listed to prove that the algorithm can approach the Pareto front more quickly and accurately than the previous al  gorithm.

Key words: continuous space;multi-objective problem;multi-objective ant-genetic algorithms（MOAGA）;Pa reto front