Research And Application Of Multi-Strategy Combination Ant Colony Algorithm

The ant colony algorithm is a heuristic intelligent algorithm that simulates the natural ant foraging process to search for the optimal solution.Because the algorithm has a positive feedback mechanism,good global optimization ability,and rich population diversity,it is widely used to solve Complex optimization problems,such as Travelling Salesman Problem(TSP)and mobile robot path planning problem.Among them,the TSP problem is a classic NP-hard combination optimization problem,which has theoretical feasibility research value,the mobile robot path planning problem is the core problem in robot technology,and has practical application research value.Therefore,this article will study the improvement of the ant colony algorithm and its application in the optimization problem.The main work contents are as follows:In view of the situation that the classic ant colony algorithm has too slow convergence speed and the algorithm is easy to fall into the local optimum,this paper proposes an adaptive update strategy(Adaptive update-Ant colony system,AU-ACS).The improved strategies include: Adaptive local pheromone update strategy,improved global pheromone update strategy,and improved subpath contribution.First,the AU-ACS algorithm obtains the optimal solution of the TSP problem by maintaining the balance between the algorithm's population diversity and convergence speed.Second,the AU-ACS algorithm increases the diversity of population search through an improved local pheromone update strategy.Then,the AU-ACS algorithm accelerates the convergence rate of the algorithm through an improved global pheromone update strategy.Next,the improved subpath contribution is used to optimize the optimal path found by the population.Finally,the proposed AU-ACS algorithm is applied to solve the TSP problem,and the improvement effect of the AU-ACS algorithm compared with the classic ant colony algorithm is analyzed to verify the effectiveness of the AU-ACS algorithm.In order to deal with large-scale TSP problems,the classic ant colony algorithm increases the calculation time of the algorithm,and the accuracy of the solution will decrease.Therefore,this paper proposes a layered and progressive improved clustering ant colony algorithm(Density peak clustering algorithm-ant colony system,DP-ACS),the DP-ACS algorithm is mainly composed of an improved density peak clustering operator,an improved pheromone update strategy,and a 3-Opt(3-optimization)operator.First,the DP-ACS algorithm uses an improved density peak clustering operator to select the clustering center.Second,the algorithm cuts the TSP problem according to the clustering center to form a new small-scale TSP problem community.Then,the improved pheromone update strategy of ant colony algorithm is used to solve the small-scale TSP problem community,thereby forming multiple unrelated sub-path loops.Then,the reconnection process of the nearest neighbor principle and the optimization of the 3-Opt operator form the global path of the original TSP problem.Finally,the simulation further analyzes the population diversity of the DP-ACS algorithm and the iterative effect of the algorithm operation,and concludes that the DP-ACS algorithm is effective for solving large-scale TSP problems,thereby verifying the performance of the DP-ACS algorithm.Finally,in order to further verify the effect of the improved ant colony algorithm in practical applications,this paper applies the proposed AU-ACS algorithm and DP-ACS algorithm to the Turtlebot2 robot path planning problem.By analyzing the effect of the AU-ACS algorithm and DP-ACS algorithm on the robot path planning problem,the practicability of the improved ant colony algorithm to solve the robot path planning problem is verified.