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2005•Transactions of the Korean Society of Mechanical Engineers ARequires access

A Study on Optimal Design of Rocker Arm Shaft Using Improved Genetic Algorithm

Soo Jin Lee, Yong Su An, Dong Woo Lee, Seok-Swoo Cho, Won-Sik Joo

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Abstract

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a evaluation function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

About this research paper

What this paper is about

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a evaluation function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

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Available abstract

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a evaluation function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

Key concepts: Genetic algorithm, Population-based incremental learning, Meta-optimization, Simple (philosophy), Algorithm, Convergence (economics), Cultural algorithm, Computer science

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