An improved simulated annealing andgenetic algorithm for TSP
Ye Gao, Xue Rui
Abstract
Ye Gao, Xue Rui
Abstract
In order to improve the evolution efficiency and species diversity of traditional genetic algorithm in solving TSP problems, a modified hybrid simulated annealing genetic algorithm is proposed. This algorithm adopts the elite selection operator to ensure not only the diversity of the algorithm but also that groups are always close to the optimal solution; at the same time, places the simulated annealing algorithm in the evolutionary process of genetic algorithm, and using the hybrid algorithm dual criteria to control algorithm's optimize performance and efficiency simultaneously. The final example shows that the hybrid algorithm is an optimization method with higher optimize performance, efficiency and reliability.
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In order to improve the evolution efficiency and species diversity of traditional genetic algorithm in solving TSP problems, a modified hybrid simulated annealing genetic algorithm is proposed. This algorithm adopts the elite selection operator to ensure not only the diversity of the algorithm but also that groups are always close to the optimal solution; at the same time, places the simulated annealing algorithm in the evolutionary process of genetic algorithm, and using the hybrid algorithm dual criteria to control algorithm's optimize performance and efficiency simultaneously. The final example shows that the hybrid algorithm is an optimization method with higher optimize performance, efficiency and reliability.
Key concepts: Simulated annealing, Adaptive simulated annealing, Algorithm, Computer science, Genetic algorithm, Evolutionary algorithm, Hybrid algorithm (constraint satisfaction), Mathematical optimization