Metaheuristic approaches to tool selection optimisation
Metaheuristic approaches to tool selection optimisation
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DOI：
10.1145/2330163.2330313
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发表时间：
2012-07
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影响因子：
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通讯作者：
Alexander W. Churchill;P. Husbands;Andrew O. Philippides
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文献类型：
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作者：
Alexander W. Churchill;P. Husbands;Andrew O. Philippides
本文讨论了解决粗加工中刀具选择问题的方法。利用仿真方法对刀具序列进行评估，从而为刀具轨迹和最终加工零件的三维模型提供了精确的数值。这允许使用不同的工具类型进行很大程度上不受限制的搜索，使这种方法比以前解决问题的尝试更适用于实际应用程序。对每个有效的刀具序列进行了穷举搜索，结果表明，相关研究中的假设可能会阻碍最优解的发现。元启发式算法由于其复杂的组合特性而被用于遍历搜索空间。测试了四种算法——遗传算法、随机爬坡算法、混合遗传算法和随机重启随机爬坡算法。评估了RRSHC在处理两个竞争需求、寻找最优解和保持低潜在昂贵评估次数方面的性能，结果表明RRSHC在解决精度方面表现最好，但计算成本最高。SHC找到最优序列的频率较低，但需要的评估要少得多，HGA介于两者之间，如果问题域没有很好地指定，它是一个很好的选择。
In this paper we discuss our approach to solving the tool selection problem, specifically applied to rough machining. A simulation is used to evaluate tool sequences, which provides accurate values for tool paths and a 3D model of the final machined part. This allows for a largely unrestricted search using different tool types, making this approach more useful for real world applications than previous attempts at solving the problem. An exhaustive search of every valid tool sequence is executed and shows that assumptions present in related research can prevent the optimal solution from being discovered. Metaheuristic algorithms are used to traverse the search space because of its complex combinatorial properties. Four algorithms are tested - Genetic Algorithm, Stochastic Hill Climbing, Hybrid Genetic Algorithm and Random Restart Stochastic Hill Climbing. Evaluating their performance at coping with two competing demands, finding optimal solutions and keeping the number of potentially expensive evaluations low, it is shown that RRSHC performs best in terms of solution accuracy but at the greatest computational cost. SHC finds the optimum sequence less frequently but needs far fewer evaluations and the HGA lies somewhere in between, making it a good choice if the problem domain is not well-specified.