Research On Quantum Ant Colony Algorithm Based On Dynamic Strategy And New Revolving Door

General quantum ant colony algorithm(QACA)has been widely used in multi-objective combinatorial optimization problems,but there are still some shortcomings.For example,the convergence speed is slow and the convergence needs many iterations in the solution of Traveling Salesman Problem(TSP).The convergence rate of the algorithm is slow and cannot be well done to complete the convergence trend in the solution of work sorting problem,thus affecting the global convergence of the algorithm.How to solve the existing defects of QACA is the urgent thing need to be solved for the relevant researchers.As the traditional QACA is easy to fall into local optimum,the convergence is slow and it requires many iterations,we proposed an improved quantum ant colony algorithm(IQACA)to solve these problems in this paper.First of all,we designed a new adaptive dynamically updating strategy of pheromone evaporation factor which changed the method acquiring valve of the pheromone evaporation factor from the fixed way to the dynamic way which controlled by the function,then dynamically update on the pheromone combining with the adaptive factor.Secondly,we replaced the traditional quantum rotation gate which affected by the rotation angle amplitude largely with a new quantum rotation gate to control the convergence trend of quantum probability amplitude,making it no longer converge to 0 or 1.The results of the TSP simulation experimental showed that both the algorithm using adaptive dynamic strategy and the algorithm using a new quantum rotation door had better performance than traditional QACA algorithm.Finally,a new QACA algorithm which use dynamic updating strategy and new rotation gate simultaneously was proposed.Experimental results based on basic functions and TSP problems show that the performance of the new algorithm was better than tradition QACA algorithms and it can effectively avoid the local optimum for the algorithm.The research content,research methods and research conclusion involved in this article are the expansion and exploration for QACA to solve combinatorial optimization problems,which can provide a certain theory foundation and reference value for the relevant researchers.