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Improvement of Genetic Algorithm for TSP and Its Parallelization Study

Author: HouJianHua
Tutor: LuoShengXian
School: Chengdu University of Technology
Course: Applied Mathematics
Keywords: Genetic Algorithms NP-complete problems Combinatorial optimization Traveling Salesman Problem MPI parallel algorithm
CLC: O241
Type: Master's thesis
Year: 2004
Downloads: 577
Quote: 8
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Genetic algorithm search algorithm , a simulation of natural biological evolution because it is simple , robust and strong , especially in the specialized field of knowledge is not required and only the fitness function evaluation to guide the search process , the application so that it extremely broad , and has been in a number of areas of practical application , has made remarkable achievements , attracted the attention of the majority of scholars and engineers . Genetic algorithms are an emerging technology is in the development phase . Although a good harvest in the application domain , its theoretical basis is relatively weak , there are many areas that need research and development to enrich . Some research and analytical work on the theory and application of genetic algorithms . First introduced genetic algorithm theory and its applications in combinatorial optimization problems and for solving TSP problem based on genetic algorithm , an improved hybrid genetic algorithm is proposed on the basis of the original genetic algorithm . The algorithm is introduced early in the iteration suited to function as the evaluation criteria , combined with heuristic crossover and edge recombination crossover operator to design a new crossover operator and hybrid mutation operator uses a combination of pattern variation and heuristic mutation and variability of individual immune operation . Numerical experiments show that the algorithm is effective . Finally, the problem of large to overcome genetic algorithm calculation , based on the parallel nature of the genetic algorithm to achieve a master-slave parallel hybrid genetic algorithm and experimental numerical results prove the feasibility and effectiveness of the algorithm .

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CLC: > Mathematical sciences and chemical > Mathematics > Computational Mathematics > Numerical Analysis
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