The Application Of Improved Immune Genetic Algorithm In Combination Optimization Problems

On the one hand,With the development of computer technology,Genetic and ant colony optimization heuristic algorithm is being more and more widely used. On the other hand, Along with the development of modern industry, How to better solve combinatorial optimization problem is not only becoming a academic research focus, and will bring immeasurable economic benefits. based on the basis of the previous studies, this paper aimed at doing some research about how to better solve combinatorial optimization problem with GA.Try to acquire the global optimal solution of combinatorial optimization problem through the improvement of GA especially,This paper combined with practical application example of CVRP, FJSP to do specific researches. To verify the effect of Improved Genetic Algorithm. This paper is divided into the following six chapters to do a progressive research step by step:Part1:introduces the background and significance the research status of domestic and foreign genetic algorithm and combinatorial optimization problem at present stage.Part2:introduce Basic principle, the basic concept, the operation process, the existing problems of GA and IGA(Immune genetic algorithm)、The biological principle of immune genetic algorithm and other commonly used methods to solve combinatorial optimization problem.Part3:against the two kinds of improved Genetic algorithm presented in this paper-Adaptive and clonally selection immune genetic algorithm,to do a overall and detailed introduction and analysis about their principles, operation processes, basic formulas. This is the focus of this paperPart4:Undertake part3, Combined with the typical example, this part makes a practical Validation for solving CVRP problem through clonally selection immune genetic algorithm.Part5:Undertake part3, Combined with the typical example, this part makes a practical Validation for solving FJSP problem through clonally selection immune genetic algorithm.Part6:This part summarizes the research work to get the conclusion, and discusses the future research work.This paper put forward clonal selection immune genetic algorithm and adaptive immune genetic algorithm for solving CVRP and FJSP based on the basic genetic algorithm. Immune clonal selection genetic algorithm through cloning approach avoids the cross process resulting in inconsistent with coding rules phenomenon. And through the high frequency inversion operation variation method improves the individual variation in the frequency, so as to avoid falling into local optimal algorithm. In addition, the method through immune injection and extraction method of population evolution makes efficient vaccine.Adaptive immune genetic algorithm uses a dynamic adaptive extraction vaccine strategy, according to the similarity of the derived population adaptive crossover probability and mutation probability.