2013Unpublished venueRequires access

An improved simulated annealing andgenetic algorithm for TSP

Ye Gao, Xue Rui

Open publisher page 17 citations

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.

About this research paper

What this paper is about

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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OpenAlex reports 17 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Simulated annealing, Adaptive simulated annealing, Algorithm, Computer science, Genetic algorithm, Evolutionary algorithm, Hybrid algorithm (constraint satisfaction), Mathematical optimization

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