The Research To Improve Multiple Population Genetic Algorithm And Its Application In Vehicle Routing Optimization

Genetic algorithm is a kind of random search method of simulating Darwin's genetic selection and biological evolution process of natural selection,which is widely applied as soon.However genetic algorithm also has many problems,such as computational cost is too high,the slow convergence speed,the population cover the search area cannot be very good and easy to trap in local optimal solution.Parallel genetic population is a method of more superior performance in the improved genetic algorithm which proposed in recent years.The basic idea is to use more child population instead of the original single,make the good coverage population search area and the algorithm of searching efficiency is greatly improved.But at present most scholars do not have too much improvement in communication between the population,In order to play out the role of communication really and make up for the shortage,this paper make improvement under the multi-population framework and design a multiple population genetic algorithm based on cross accessibility evaluation.By combining with simulated annealing algorithm to improve the local search ability of the algorithm,and designed a kind of cross accessibility evaluation communication operator,which means choosing the communication individuals in population and selecting the best offspring through crossover operation,then through evaluating affinity to replace individuals of the target populations,make the population still has the good diversity in middle and later periods,avoid falling into local optimum.Finally,choose a variety of commonly used methods and the method in this paper to optimize of the six functions at the same time,according to the final results verifies the rationality of the algorithm and the superiority of performance in this paper.Vehicle routing problem(VRP)is one of the key problems in the process of logistics distribution,the problem received extensive attention of scholars both at home and abroad when it was proposed.VRP evolved from traveling salesman problem(TSP),TSP is a special case of VRP,which included only one path and no capacity constraints.Optimization algorithm has been widely used in the VRP,therefore applied the improved multiple population genetic algorithm in this paper to solving specific VRP(TSP)and traditional VRP problem,through mathematical modeling,combining the VRP and the TSP problem,set up the reasonable interface with algorithm,verify the validity of the algorithm.Through contrast experiment,the results shows that the improved algorithm has better global and local search ability,can quickly converge to the best driving route,and find the path is better,has the commendable practical value.