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2010•Journal of Jilin Institute of Architecture & Civil EngineeringRequires access

Structural Optimization Design Based on Genetic Simulated Annealing Algorithm

Zhu Chao-yan

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Abstract

Genetic algorithm(GA),simulated annealing(SA) are both stochastic search method,and they have the excellent predominance to compare the traditional structural optimization algorithms when they solve the diff iculties just like dealing with global optimization,discrete variables and the multi-connected feasible area.This essay is aimed at the characteristics of GA and SA,and combined the advantages to make a hybrid genetic algorithm---GSA.This improved GSA has both the great global search ability of GA and the excellent effect of the local optimization of SA.

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What this paper is about

Genetic algorithm(GA),simulated annealing(SA) are both stochastic search method,and they have the excellent predominance to compare the traditional structural optimization algorithms when they solve the diff iculties just like dealing with global optimization,discrete variables and the multi-connected feasible area.This essay is aimed at the characteristics of GA and SA,and combined the advantages to make a hybrid genetic algorithm---GSA.This improved GSA has both the great global search ability of GA and the excellent effect of the local optimization of SA.

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Available abstract

Genetic algorithm(GA),simulated annealing(SA) are both stochastic search method,and they have the excellent predominance to compare the traditional structural optimization algorithms when they solve the diff iculties just like dealing with global optimization,discrete variables and the multi-connected feasible area.This essay is aimed at the characteristics of GA and SA,and combined the advantages to make a hybrid genetic algorithm---GSA.This improved GSA has both the great global search ability of GA and the excellent effect of the local optimization of SA.

Key concepts: Simulated annealing, Global optimization, Mathematical optimization, Meta-optimization, Genetic algorithm, Optimization algorithm, Computer science, Metaheuristic

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