以啟發式演算法改善發光二極體之照明效果

Improving the Lighting Performance of an LED through a Heuristic Procedure

徐志明(Chih-Ming Hsu)；湯韶元(Shao-Yuan Tang)；洪慈鈞(Tzu-Chun Hung)

發光二極體 ； 反光杯 ； 螢光粉 ； 基因規劃 ； 蟻群演算法 ； 多重品質特性參數設計 ； Light-emitting diode ； Reflector ； Phosphor ； Genetic programming ； Ant colony optimization ； Multi-response parameter design

明新學報

39卷1期（2013 / 02 / 01）

173 - 191

繁體中文

發光二極體（light-emitting diodes, LEDs）已經廣泛地應用於不同的照明場所中。發光二極體的照明效果會受到其反光杯幾何形狀設計的顯著影響。在過去，設計工程師會根據光學原理及自己的經驗以決定反光杯的幾何設計，然後，透過實際生產一些反光杯以驗證其設計之可行性與照明效果，這種嘗試錯誤法不但極為花費成本且浪費時間。此外，這種方法並無法保證其所設計的反光杯幾何尺寸為最佳設計。因此，在發光二極體反光杯幾何設計上所面臨的參數設計問題是一個亟待解決的課題。由於發光二極體的整體照明效果必須透過幾種品質特性加以衡量，而這些品質特性又會受到反光杯的數個關鍵幾何設計參數和螢光粉濃度所影響，因此，它是一個較為複雜的多重品質特性參數設計問題。本研究利用基因規劃（genetic programming, GP）與連續值最佳化的蟻群演算法（ant colony optimization, ACO）提出一個系統化的方法，以解決多重品質特性參數設計問題。接著，利用一個改善反光杯設計的案例測試所提出最佳化程序的可行性與有效性。

Light-emitting diodes (LEDs) have been extensively applied in diverse lighting areas. The lighting performance of LEDs is significantly influenced by the geometric design of a reflector. In the past, LED design engineers have usually determined the geometric design of a reflector based on the optical principles and their own experience. The feasibility and performance of a certain design are verified through creating some real reflectors. This trial-and-error procedure is costly and time-consuming. In addition, it cannot be proven that the geometric design of a reflector is really optimal. Consequently, it is crucial to resolve the problems arising in the geometric design of a reflector as soon as possible. The performance of an LED is evaluated through several critical quality characteristics which are affected by some geometric design parameters of a reflector. Hence, this is a complicated multi-response parameter design problem. This study proposes a systematic approach based on genetic programming (GP) and ant colony optimization (ACO) for continuous domains to deal with the parameter design problems with multiple responses. A case study on improving the design of an LED reflector is used to demonstrate the feasibility and effectiveness of the proposed approach.

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