2006Journal of Beijing University of TechnologyRequires access

A Hybrid Strategy Based on Genetic Algorithm and Tabu Search

Yanfeng Sun

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Abstract

Genetic algorithm and tabu search algorithm are powerful tools to solve the complicated large-scale optimization problems. Through comprehensive contrast and comparison between the above two algorithms, a hybrid optimization algorithm was proposed to improve the local search ability of genetic algorithm. In this algorithm, in order to speed up convergence speed and get satisfied results, tabu search algorithm was applied for local search, and genetic algorithm was used for global search. Meanwhile a strategy was proposed to control prematurity and to avoid converging to local optimum. The test results show that both calculating speed and output are improved

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What this paper is about

Genetic algorithm and tabu search algorithm are powerful tools to solve the complicated large-scale optimization problems. Through comprehensive contrast and comparison between the above two algorithms, a hybrid optimization algorithm was proposed to improve the local search ability of genetic algorithm. In this algorithm, in order to speed up convergence speed and get satisfied results, tabu search algorithm was applied for local search, and genetic algorithm was used for global search. Meanwhile a strategy was proposed to control prematurity and to avoid converging to local optimum. The test results show that both calculating speed and output are improved

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Available abstract

Genetic algorithm and tabu search algorithm are powerful tools to solve the complicated large-scale optimization problems. Through comprehensive contrast and comparison between the above two algorithms, a hybrid optimization algorithm was proposed to improve the local search ability of genetic algorithm. In this algorithm, in order to speed up convergence speed and get satisfied results, tabu search algorithm was applied for local search, and genetic algorithm was used for global search. Meanwhile a strategy was proposed to control prematurity and to avoid converging to local optimum. The test results show that both calculating speed and output are improved

Key concepts: Tabu search, Guided Local Search, Hill climbing, Population-based incremental learning, Local search (optimization), Mathematical optimization, Genetic algorithm, Best-first search

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