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Optimal design of four-bar mechanisms using a hybrid multi-objective GA with adaptive local search

机译:混合多目标遗传算法与自适应局部搜索的四连杆机构优化设计

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摘要

Responding to an increasing demand for mechanism synthesis tools that are both efficient and accurate, this paper presents a novel approach to the multi-objective optimal design of four-bar linkages for path-generation purposes. Three, often conflicting criteria including the mechanism's tracking error, deviation of its transmission angle from 90° and its maximum angular velocity ratio are considered as objectives of the optimization problem. To accelerate the search in the highly multimodal solution space, a hybrid Pareto genetic algorithm with a built-in adaptive local search is employed which extends its exploration to an adaptively adjusted neighborhood of promising points. The efficiency of the proposed algorithm is demonstrated by applying it to a classical design problem for one, two and three objective functions and comparing the results with those reported in the literature. The comparison shows that the proposed algorithm distinctly outperforms other algorithms both quantitatively and qualitatively (from a practical point of view).
机译:为了满足对高效而准确的机构综合工具的日益增长的需求,本文提出了一种新颖的方法,以达到生成路径的目的,对四连杆机构进行多目标优化设计。包括机械的跟踪误差,传动角与90°的偏差以及最大角速度比在内的三个经常相互冲突的标准被视为优化问题的目标。为了加快在高度多模态解空间中的搜索,采用了带有内置自适应局部搜索的混合Pareto遗传算法,该算法将其探索扩展到了自适应调整的有希望点的邻域中。通过将其应用于一,二,三目标函数的经典设计问题,并将结果与​​文献报道相比较,证明了所提算法的效率。比较表明,从实用角度看,该算法在数量和质量上都明显优于其他算法。

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