2008Hoisting and Conveying MachineryRequires access

Optimal design of gear transmission based on hybrid genetic algorithm

Yang Jian-ju

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Abstract

Mathematical model of gear transmission optimization design is built with minimum volume as objective function.The optimization problem is transformed into non-restraint optimization problem using the outer penalty function method.Considering limitation of ordinary genetic algorithm,hybrid genetic algorithm is put forward and used,in which hybrid coding based on integer and real coding is adopted,fitness function is adjusted,random parent-number fitness-weighted crossover and adaptive mutation methods are adopted,simulated annealing algorithm is added to,and method of determining initial temperature is given.The hybrid genetic algorithm features less infeasible solution,higher convergence speed and can avoid premature convergence.Optimization results show that the algorithm is practical and efficient.

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Mathematical model of gear transmission optimization design is built with minimum volume as objective function.The optimization problem is transformed into non-restraint optimization problem using the outer penalty function method.Considering limitation of ordinary genetic algorithm,hybrid genetic algorithm is put forward and used,in which hybrid coding based on integer and real coding is adopted,fitness function is adjusted,random parent-number fitness-weighted crossover and adaptive mutation methods are adopted,simulated annealing algorithm is added to,and method of determining initial temperature is given.The hybrid genetic algorithm features less infeasible solution,higher convergence speed and can avoid premature convergence.Optimization results show that the algorithm is practical and efficient.

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Available abstract

Mathematical model of gear transmission optimization design is built with minimum volume as objective function.The optimization problem is transformed into non-restraint optimization problem using the outer penalty function method.Considering limitation of ordinary genetic algorithm,hybrid genetic algorithm is put forward and used,in which hybrid coding based on integer and real coding is adopted,fitness function is adjusted,random parent-number fitness-weighted crossover and adaptive mutation methods are adopted,simulated annealing algorithm is added to,and method of determining initial temperature is given.The hybrid genetic algorithm features less infeasible solution,higher convergence speed and can avoid premature convergence.Optimization results show that the algorithm is practical and efficient.

Key concepts: Crossover, Mathematical optimization, Meta-optimization, Penalty method, Fitness function, Genetic algorithm, Coding (social sciences), Simulated annealing

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