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A Fibonacc Hybrid Genetic Algorithms of Structural Optimization with Discrete Variables

Peng Guo

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Abstract

A Fibonacc search method was presented for structural optimization with the discrete variables. Through defining a Fibonacc operator in the genetic algorithm, a hybrid genetic algorithm for structural optimization with discrete variables, combing the advances of both genetic algorithm and the Fibonacc design method, was proposed. The constrained optimization problems were dealt with by adaptive annealing penalty factors and penalty function. The numerical results show that the hybrid genetic algorithm has a rather high convergence speed, but also can locate the global optimum with a rather large probability to obtain structural optimum design with discrete variables.

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A Fibonacc search method was presented for structural optimization with the discrete variables. Through defining a Fibonacc operator in the genetic algorithm, a hybrid genetic algorithm for structural optimization with discrete variables, combing the advances of both genetic algorithm and the Fibonacc design method, was proposed. The constrained optimization problems were dealt with by adaptive annealing penalty factors and penalty function. The numerical results show that the hybrid genetic algorithm has a rather high convergence speed, but also can locate the global optimum with a rather large probability to obtain structural optimum design with discrete variables.

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Available abstract

A Fibonacc search method was presented for structural optimization with the discrete variables. Through defining a Fibonacc operator in the genetic algorithm, a hybrid genetic algorithm for structural optimization with discrete variables, combing the advances of both genetic algorithm and the Fibonacc design method, was proposed. The constrained optimization problems were dealt with by adaptive annealing penalty factors and penalty function. The numerical results show that the hybrid genetic algorithm has a rather high convergence speed, but also can locate the global optimum with a rather large probability to obtain structural optimum design with discrete variables.

Key concepts: Simulated annealing, Mathematical optimization, Penalty method, Meta-optimization, Combing, Genetic algorithm, Algorithm, Convergence (economics)

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