2013Computer Engineering and Applications JournalOpen access

Improved simulated annealing algorithm and genetic algorithm for TSP

Yao Mingha

Open full text 8 citations

Abstract

The characteristics of genetic algorithm and simulated annealing algorithm are compared, it elaborates the necessity of the combination of genetic algorithm and simulated annealing algorithm. An improved simulated annealing and genetic algorithm for solving TSP is proposed. The global random searching ability of genetic algorithm makes up the question of the simulated annealing algorithm that easy to fall into the local optimal solution. The crossover method of genetic algorithm is changed,the parent chromosomes and offspring chromosomes are crossed, it solves the problems of traditional genetic algorithmpremature. It proposes new solution generation mechanisms and improved algorithm for that the simulated annealing algorithm converges slowly, the method improves the speed of convergence of the algorithm. Experimental test results show that the new algorithm has faster convergence and better stability.

About this research paper

What this paper is about

The characteristics of genetic algorithm and simulated annealing algorithm are compared, it elaborates the necessity of the combination of genetic algorithm and simulated annealing algorithm. An improved simulated annealing and genetic algorithm for solving TSP is proposed. The global random searching ability of genetic algorithm makes up the question of the simulated annealing algorithm that easy to fall into the local optimal solution. The crossover method of genetic algorithm is changed,the parent chromosomes and offspring chromosomes are crossed, it solves the problems of traditional genetic algorithmpremature. It proposes new solution generation mechanisms and improved algorithm for that the simulated annealing algorithm converges slowly, the method improves the speed of convergence of the algorithm. Experimental test results show that the new algorithm has faster convergence and better stability.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The characteristics of genetic algorithm and simulated annealing algorithm are compared, it elaborates the necessity of the combination of genetic algorithm and simulated annealing algorithm. An improved simulated annealing and genetic algorithm for solving TSP is proposed. The global random searching ability of genetic algorithm makes up the question of the simulated annealing algorithm that easy to fall into the local optimal solution. The crossover method of genetic algorithm is changed,the parent chromosomes and offspring chromosomes are crossed, it solves the problems of traditional genetic algorithmpremature. It proposes new solution generation mechanisms and improved algorithm for that the simulated annealing algorithm converges slowly, the method improves the speed of convergence of the algorithm. Experimental test results show that the new algorithm has faster convergence and better stability.

Key concepts: Simulated annealing, Adaptive simulated annealing, Algorithm, Crossover, Population-based incremental learning, Genetic algorithm, Computer science, Convergence (economics)

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