Researches On An Improved Multi-Objective Ant Colony Optimization Algorithm For Multi-Objective Traveling Salesman Problems

In the research of science and engineering application,the optimization problem often has multiple objective functions.Multi-objective optimization has become an important research topic in the field of operations researches.Early multiobjective optimization theory focused on the so-called "prior" approach,in other words,it is necessary to know the priori preference information of different objective functions in advance.However,in many practical problems,it is difficult to pre-determine the preference information of each objective function,so more and more researchers begin to adopt the "posterior" mechanism to solve the multi-objective optimization problem.The goal is to obtain as many Pareto optimal solutions as possible so that decision makers can choose their preferred solutions.As the optimization mechanism based on population iteration has the opportunity to obtain multiple Pareto optimal solutions through one operation,Evolutionary algorithms are becoming one of the mainstream algorithms for solving multi-objective optimization problems in recent years.The corresponding research field is called evolutionary multi-objective optimizationBased on this,this paper adopts the theory of systems engineering and applies the theories and methods of operations research,computational science and engineering mathematics,to study the ant colony algorithm applied to solve the multi-objective traveling salesman problem.A new multi-objective optimization method will be proposed.In this paper,the main research work includes the following three aspects:1)The research status of evolutionary multiobjective optimization,especially multiobjective ant colony algorithm is reviewed,and the advantages and disadvantages of the existing algorithms are analyzed;2)on the basis of review,an improved hybrid multi-objective ant colony algorithm is proposed by effectively integrating the design ideas of two multi-objective evolutionary algorithms 3)The performance of the proposed algorithm is tested by a series of multi-objective TSP test cases constructed based on the standard TSPLIB example,and the sensitivity analysis of the key parameters in the algorithm design is carried out.By comparing with the existing multi-objective ant colony algorithm,we can find that the new multi-objective ant colony algorithm proposed in this paper can obtain the Pareto solution set with better distribution and convergence.