Applying Hierarchical Genetic Algorithm Based Neural Network and Multiple Objective Evolutionary Algorithm to Optimize Parameter Design with Dynamic Characteristics

以階層式基因演算法與多目標演化演算法進行動態製程之最佳參數設計

馬心怡(Hsin-Yi Ma)；蘇朝墩(Chao-Ton Su)

參數設計 ； 階層式基因演算法 ； 多目標演化演算法 ； parameter design ； hierarchical genetic algorithm HGA ； multiple objective evolutionary algorithm MOEA

品質學報

17卷4期（2010 / 08 / 31）

311 - 325

英文

田口之參數設計是一公認爲具有極大貢獻之品質技術，但是參數設計卻有一些先天之限制使其使用受限，因此許多學者以類神經網路(neural networks, NNs)提出修正模式。然而對於動態特性之問題，其靈敏度與品質變異常無法同時到達最佳化之目標，且傳統使用之類神經網路無法保證可以獲得參數與品質特性間可靠的對應關係。本研究提出一演算法，結合階層式基因演算法(hierarchical genetic algorithm, HGA)與多目標演化演算法(multiple objective evolutionary algorithm, MOEA)進行動態製程之最佳化程序，以解決上述問題。最後並以一塑膠射出成型之製程爲例，以驗證本方法之有效性。

Many soft computing techniques were used to resolve Taguchi's parameter design problems. These methods consist of two major steps where neural networks are first adopted to find the functional relationship between the desired responses and control factor values and then simulated annealing or genetic algorithm is applied to determine an optimal combination of control factors. However, neural networks tend to trap the error function in a local minimum when one tries to find the parameters of the network. Besides, the sensitivity measure and variability measure need to be optimized simultaneously in a dynamic system. In this paper, we integrate a hierarchical genetic algorithm (HGA) and a multiple objective evolutionary algorithm (MOEA) to optimize the dynamic parameter design problem. The proposed method applies a HGA based neural network to derive the relationship between the input factors and corresponding outputs, β and SN ratio. Then a MOEA is applied to obtain the non-dominated solution of predicted SN ratio and β. Finally, in the confirmation phase, confirmation experiments are conducted to determine the best parameter setting. An industry case of injection molding process is demonstrated to show the effectiveness and its applicability to other industries.

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