On Improved Ant Colony Algorithm For Solving Multi-objective Optimization Problems

This article bring forward two kinds of multi-objective optimization problem for solving the improved ant colony algorithm. This article describes two methods for multi-objective optimization problems of ant algorithms; for multi-objective TSP is intelligent Ant Colony algorithm, with the introduction of multi-criteria indicator, pheromone updates, on the introduction of intelligent knowledge of ants, ants each time the loop is set off from the same location, algorithm parameter setting of the four areas for multi-objective TSP traditional ant algorithms to improve simulation, by example, had demonstrated the effectiveness of the algorithm, and in theory on the convergence of the algorithm.Based on game theory multiobjective evolutionary ant algorithms is directed at the inefficiencies of ACA on basic Ant Colony algorithms evolutionary improvement made by evolutionary ACS (EvolutionaryAntColonySystem, EACS) algorithm, the model will be artificial evolution Ant as you can, with the introduction of individual choice, crossover and mutation, and so on, implementation of adaptive algorithm parameters.Make Ant algorithms become an evolutionary algorithm.Then, for multi-objective optimization features, game theory of Nash equilibrium strategy of thought and improved Ant Colony algorithms for multi-objective optimization problems.Checked out by calculation example algorithm for the high-convergence.Finally, the algorithm from the convergence of the algorithm.