多品質目標製程最佳化演算法

Optimization Multiple Quality Objective Algorithms

10.6220/joq.2012.19(5).02

王嘉興(Dja-Shin Wang)；鍾青原(Ching-Yuan Jhong)；古東源(Tong-Yuan Koo)

多品質特性 ； 多目標最佳化 ； 演算法 ； multiple quality characteristics ； optimization multiple objective ； algorithms

品質學報

19卷5期（2012 / 10 / 01）

423 - 443

繁體中文

現今複雜的製造程序中，企業經常需要同時最佳化多個品質特性，因此急需建立一套有效的方法，來找出同時兼顧多品質特性的製程最佳化參數組合；本研究目的即是快速解決多品質目標製程最佳化的問題。所提出方法是改良比例分配多目標粒子群演算法模型（Improved Proportional Distribution for Multi-objective Particle Swarm Optimization, IPD-MOPSO），可以適用於二個目標以上的多目標最佳化上，並同時加入次佳解機制。研究發現所搜尋出的非凌駕解能分佈更廣，而且避免掉落入局部最佳解，可獲得柏拉圖最佳解群體。實例驗證結果顯示，提供的新演算法，使用於二個目標以上的最佳化問題，皆能更均勻逼近柏拉圖前緣的柏拉圖最佳解集合。對多品質目標製程最佳化的問題，能提供工程師快速獲得最佳參數組合，對高科技產業多品質特性最佳化有顯著貢獻與效益。

In the intense competition nowadays, the industrial productions must have optimization multiple quality characteristics simultaneously. Therefore, the engineers need a methodology to solve these problems. In this paper, we proposed a new algorithm, say, Improved Proportional Distribution for Multi-objective Particle Swarm Optimization (IPD-MOPSO), was expanded to solve the problem with more than two objectives. The results show that can find out Pareto optimal solution set, and the second optimization solution mechanism is added to search nearby space besides of non-dominated solutions effectively. Two practical examples were presented to illustrate proposed approach, from the results of the solution search, finding out object solution space and Pareto-optimal solutions set. It can provide engineers/managers to find out optimization parametric design of multiple quality characteristics, and that have significant value on practical high technology industrial manufacture process.

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