A Novel Improved Genetic Algorithm and Application in Mechanical Optimal Design

Abstract:

A new improved genetic algorithm (IGA) based on floating point encoding is proposed. Firstly, IGA uses information entropy to produce better initialized species population. Secondly, after synthetically studying the searching properties of crossover operator in GA, it designs a new crossover strategy that effectively increases searching efficiencies of IGA. Thirdly, to avoid searching being trapped in local minimum, it designs a chaos degenerate mutation operator that makes the searching fast converge to a global minimum. At last IGA is used to solve the problem of the optimal design to crane girder, which is a typical problem of mechanical optimal design. Compared with the traditional random direction method, neural network method, genetic-neural network method, hybrid genetic algorithm, chaos-GA, PSO algorithm, chaos-PSO algorithm and standard GA, IGA shows better performance at the aspect of solution precision and convergence speed than that of these algorithms.

• Periodical: Materials Science Forum (Volumes 628-629)
• Pages: 263-268
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.628-629.263
• Online since: August 2009
• Authors: Zhong Min Wang, Yu Jun Cai, D.H. Miao
• Keywords: Chaos, Crane Girder, Genetic Algorithm (GA), Information Entropy, Optimization Design

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