Multi-population Ant Colony Algorithm Based On Competition And Cooperation Mechanism And Its Application

With the gradual improvement of social productivity,mobile robots have played a huge role in various fields,among which path planning is one of the important technologies of mobile robots.Ant colony optimization algorithm,as a classic swarm intelligence algorithm,was early applied to solve the traveling salesman problem,and then gradually used to solve various optimization problems(including robot path planning problems,etc.).This article mainly studies the theory and application of ant colony optimization.The performance of the algorithm is discussed first using the traveling salesman problem,and then the feasibility of the algorithm is discussed using the robot path planning problem.The research contents of this article are as follows:First of all,in order to improve the diversity of the single population algorithm,an ant colony optimization algorithm is proposed which integrates dynamic scheduling strategy and competition mechanism.In the process of ants selecting paths,construct a scheduling strategy,set the scheduling operator to monitor the number of ants on the path in real time,and dynamically adjust the direction of ants to explore the path according to the number of iterations.Then use the subgroup competition mechanism to give information to the winning subgroup.Prime incentives,follow-up strategies for the failed subgroups of competition,and lead to the development of advantages,thereby accelerating the convergence rate of the population.Finally,the effectiveness of the algorithm is verified by solving traveling salesman problems of different sizes.Aiming at the problem that single-group algorithm is prone to slow convergence and low accuracy when solving large-scale travelling salesman problems,this paper proposes an improved multiple-group ant colony optimization algorithm.First of all,in order to make up for the shortcoming of single population ant colony algorithm easily falling into local optimum,the cuckoo algorithm and ant colony algorithm are fused,and an interactive optimization algorithm based on cuckoo and ant colony is proposed.First use ant colony algorithm for the first round of optimization.Based on the ant colony algorithm's optimization information,use the improved cuckoo algorithm for the second round of optimization to further optimize the optimal solution.Secondly,an ant colony algorithm based on the fusion of dynamic evolution and interactive learning mechanism was proposed using niche technology.Based on the idea of niche,a dynamic evolution model of the population is established,the population is initialized using the good point set theory,and an interactive learning mechanism is established between the populations to ensure that the population communication efficiency is maximized.Further,inspired by the artificial fish swarm algorithm,an ant colony algorithm based on crowding degree and co-evolution mechanism was proposed.The congestion factor in the artificial fish swarm algorithm was introduced into the path construction function to control the number of ants who chose the path,which was converging.Find a balance between speed and diversity,and establish a coevolution mechanism to exchange and share optimization information of multiple groups to improve the efficiency of population solution.Experimental results show that the algorithm proposed in this paper can be optimized with higher efficiency.Finally,based on the above theoretical analysis,the algorithm proposed in this paper is applied to the robot path planning problem.Simulation experiments are performed for different types of grid maps to analyze the stability of the algorithm;the validity of the algorithm is verified by constructing a real environment map.The experimental results show that the improved algorithm proposed in this paper can effectively solve the problem of robot path planning and has high stability.