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2008•Journal of Kunming University of Science and TechnologyRequires access

A Hybrid Algorithm Based on Genetic Algorithm and Simulated Annealing Algorithm

Gao Cheng-xiu

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Abstract

By embedding simulated annealing operator into genetic algorithm,a hybrid algorithm is put forward,which assimilates advantages of both genetic algorithm and simulated annealing algorithm.Penalty function is adopted to deal with constraint conditions.Specific genetic algorithm operators are also designed to construct fitness function.The optimal control of discrete time system is therefore realized.It is proved that this algorithm can converge not only quickly but also to the optimal solution.

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What this paper is about

By embedding simulated annealing operator into genetic algorithm,a hybrid algorithm is put forward,which assimilates advantages of both genetic algorithm and simulated annealing algorithm.Penalty function is adopted to deal with constraint conditions.Specific genetic algorithm operators are also designed to construct fitness function.The optimal control of discrete time system is therefore realized.It is proved that this algorithm can converge not only quickly but also to the optimal solution.

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Available abstract

By embedding simulated annealing operator into genetic algorithm,a hybrid algorithm is put forward,which assimilates advantages of both genetic algorithm and simulated annealing algorithm.Penalty function is adopted to deal with constraint conditions.Specific genetic algorithm operators are also designed to construct fitness function.The optimal control of discrete time system is therefore realized.It is proved that this algorithm can converge not only quickly but also to the optimal solution.

Key concepts: Algorithm, Simulated annealing, Genetic algorithm, Computer science, Hybrid algorithm (constraint satisfaction), Population-based incremental learning, Adaptive simulated annealing, Fitness function

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