首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science >Dimensional synthesis of a five-point double-toggle mould clamping mechanism using a genetic algorithm-differential evolution hybrid algorithm
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Dimensional synthesis of a five-point double-toggle mould clamping mechanism using a genetic algorithm-differential evolution hybrid algorithm

机译:基于遗传算法-差分进化混合算法的五点双模合模机构尺寸综合

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摘要

A hybrid evolutionary algorithm by combining real-valued genetic algorithm (RGA) with differential evolution (DE) has been proposed by the first author. This is termed the 'GA-DE hybrid algorithm'. The only difference between the GA-DE hybrid algorithm and the RGA is in the content of the crossover. The crossover operation in the RGA is replaced by differential vector perturbation, with the best individual or some excellent individuals as the base vectors. In this work, the GA-DE hybrid evolutionary algorithm and the RGA with arithmetic crossover are employed to solve the optimization problem of the dimensional synthesis of the five-point double-toggle mould clamping mechanism with the performance of thrust saving for the prescribed input and output strokes. The optimization design task is relatively unique, because the number of constraints is considerable and the constraints are uneven. A technique for proper handling of design constraints is presented. The synthesized results are estimated using the solutions obtained by the parametric study based on an exhaustive search. Findings show that the GA-DE hybrid algorithm can successfully find a better objective function value more efficiently than the RGA with arithmetic crossover.
机译:第一作者提出了一种将实值遗传算法(RGA)与微分进化(DE)相结合的混合进化算法。这被称为“ GA-DE混合算法”。 GA-DE混合算法和RGA之间的唯一区别在于交叉的内容。 RGA中的交叉操作被微分矢量摄动所取代,以最佳个体或一些优秀个体为基础向量。本文采用GA-DE混合进化算法和带算术交叉的RGA来解决五点双肘模合模机构尺寸合成的优化问题,该结构具有节省推力的规定输入和输出。输出笔画。优化设计任务相对独特,因为约束的数量相当大且约束不均匀。提出了一种正确处理设计约束的技术。使用基于穷举搜索的参数研究获得的解估计合成结果。结果表明,与带有算术交叉的RGA相比,GA-DE混合算法可以更有效地成功找到更好的目标函数值。

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