Research And Application Of Hybrid Genetic Ant Colony Algorithm Based On Greedy Strategy

With the progress of science and technology,people are learning more and more about the essence of life,the optimization technology has gradually get rid of the calculation of classical logic arithmetic,the intelligent algorithm has developed,in which the development of bio-intelligent algorithm is relatively rapid,including genetic algorithm,ant colony algorithm,particle swarm optimization and so on.The presentation of these intelligent algorithms makes the solution of many complex problems in real life intelligent.Genetic algorithm and ant colony algorithm is undoubtedly the most classic two types of intelligent algorithms.Genetic algorithm and ant colony algorithm make the complex problem of reality,through the"evolution" in the form,meet the most approximate optimal solution.However,whether it is ant colony algorithm or genetic algorithm,the algorithm itself has some drawbacks.Genetic algorithm is a kind of intelligent algorithm which simulates the natural selection of evolutionary evolution and the process of biological evolution in genetics.It modeled and simplified the work of gene coding,in accordance with the principle of the survival of the fittest,successive evolution to produce better and better solution.However,genetic algorithms have inexplicable convergence of the algorithm itself and produce inaccurate results.In addition,the genetic algorithm is less efficient than traditional algorithms.The ant colony algorithm is a probabilistic algorithm used to find an optimal path.This algorithm has the characteristics of distributed computing,information feedback and heuristic search,which is essentially a heuristic global optimization algorithm in evolutionary algorithm.However,the ant colony algorithm is suitable for searching for path problems on the graph,and the computational overhead is very large.In this paper,a improved algorithm was proposed to improved the defects of the genetic algorithm and ant colony algorithm after in-depth study.The main work of this paper is as follows:Firstly,the background of genetic algorithm and ant colony was discussed,then the basic principle of both algorithm and the the research status of genetic algorithm and ant colony algorithm was introduced.Secondly,the basic concepts of genetic algorithm,the main steps of genetic algorithm and parameter control in genetic algorithm was introduced.Then the basic concept,main steps and related parameters of ant colony algorithm was introduced.Thirdly,a framework of hybrid genetic and ant colony algorithm based on greedy strategy was proposed,and the feasibility of greedy strategy used in intelligent algorithm,and study the hybrid method of genetic algorithm and ant colony algorithm,including the selection method of crossover operators and mutation operators in genetic algorithm,the local and global updating method of pheromone table and the choice of formula for the next node in ant colony algorithm was discussed.Finally,three specific NP-hard problems--TSP problem,job shop scheduling problem and port scheduling problem was applied with our framework,and the effectiveness,convergence and applicability of the improved algorithm were analyzed by experiment.The hybrid genetic ant colony algorithm based on greedy strategy can solve the contradiction between the convergence speed and the local optimal solution in the genetic algorithm to some extent.The greedy strategy in this paper can improve the speed converging to the optimal solution of genetic algorithm,the improved crossover operator and mutation operator of genetic algorithm can avoid premature convergence,increasing the probability of finding the optimal solution.The optimal solution found by the genetic algorithm acts on the pheromone of the ant colony algorithm,which greatly reduces the time of ant colony search,while maintaining the advantages of ant colony algorithm to find the optimal solution.After three experiments,our algorithm has the advantages of universal applicability and effectiveness.