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姓名	王鈺潔(Yu-Jie Wang)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	自適應解分享粒子群演算法及其在螺旋電感最佳化設計之應用 (Adaptive Solution-Sharing Particle Swarm Optimization and Its Application of the Design of the Spiral Inductor)
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摘要(中)	本論文中，我們提出了一種改良式粒子群演算法，名為自適應解分享粒子群演算法(Adaptive Solution-Sharing Particle Swarm Optimization, ASSPSO)，並應用於射頻積體電路(Radio Frequency Integrated Circuits, RFIC)之螺旋(Spiral)電感最佳化設計。在標準粒子群演算法中每一個體使用共同的慣性權重，且如果適應函數維度較大時，前期收斂速度會十分緩慢，為了解決這個問題，本論文使用唯一認知模型(Cognition Only Model)，以提升前期的收斂速度，並且針對慣性權重提出改良方法，利用粒子與粒子之間的差異度去更新移動方程式，可以更有效的從局部搜索開始，使粒子具有比較好的搜尋能力往全域最佳解移動，並且和幾個已經提出的改良式粒子群演算法做性能的比較，同時也顯示本文所提出的改良式粒子群演算法能有效地改善標準粒子群演算法容易陷入局部最佳解的缺點，最後應用於螺旋電感最佳化設計。在無線通訊系統中，射頻積體電路的特性好壞與電感的品質因數有密切的關係，以低雜訊放大器舉例，現階段以CMOS製程出來的螺旋(Spiral)電感之品質因素較低，使低雜訊放大器指數(NF)增加、增益(Av)下降造成電路效能不佳，所以本論文利用提出的改良式粒子群演算法有效提升電感之品質因數，以提高整個射頻積體電路(RFIC)的效能。
摘要(英)	In this thesis, we propose a variant algorithm for Particle Swarm Optimization (PSO) which is called Adapted Solution-Sharing Particle Swarm Optimization (ASSPSO), and applied to optimization of spiral inductor of Radio Frequency Integrated Circuits (RFIC). In standard PSO algorithm, each particle of SPSO using a same equation to update a particle’s velocity. If the dimension is large, the convergence rate will be very slow and get local easily. In order to solve this problem, the proposed ASSPSO uses a cognition only model to enhance the particle’s velocity and according to the distance between particle and the best particle with best fitness to adjust the inertia weight adaptively. For the particle with a better fitness, the inertia weight is decreased; otherwise the inertia weight is increased for particles with inferior fitness. The methods cause the fast convergence ability in pre-convergence and have less time on computing. The performance of ASSPSO is fairly demonstrated by applying sixteen benchmark problems and compared it with several popular PSO algorithms. Finally, the modified PSO algorithm will be applied to optimization of spiral inductor. In wireless communication systems, spiral inductors are the essential component of radio frequency integrated circuits (RFIC). The performance of radio frequency integrated circuit is decided by the quality factor (Q). Taking Low-Noise Amplifier (LNA) for example, the quality factor of LNA is lower in CMOS process. It will lead to a bad performance of circuits. So we will take advantage of modified PSO algorithm on optimization of spiral inductor to make it better.
關鍵字(中)	★ 粒子群演算法 ★ 螺旋電感	關鍵字(英)	★ Particle Swarm Optimization ★ spiral inductor
論文目次	摘要　I Abstract　II 目錄　IV 圖目錄　VI 表目錄　IX 第一章 緒論　p1 1.1研究動機　p1 1.2論文架構　p3 第二章 粒子群演算法　p4 2.1人工智能演化最佳化方法　p4 2.2傳統粒子群演算法介紹　p5 2.3粒子群演算法基本公式與模式　p5 2.4慣性權重　p6 第三章 改良式粒子群最佳化方法暨模擬　p10 3.1自適應慣性權重　p10 3.1.1自適應慣性權重原型　p11 3.1.2修正參數α　p12 3.2粒子群解分享機制　p14 3.2.1模範粒子　p14 3.2.2Refreshing Gap m　p16 3.3自適應解分享粒子群演算法　p30 3.4模擬實驗結果　p33 3.4.1測試函數10維之結果　p38 3.4.2測試函數30維之結果　p49 第四章 電感之元件特性介紹　p60 4.1電感元件上之損耗與寄生效應　p60 4.2電感自振　p60 4.3金屬損耗　p61 4.3.1導體損耗　p61 4.3.2渦狀電流損耗　p62 4.3.3集膚效應(Skin Effect)　p62 4.4基板損耗　p63 4.4.1電場穿透　p63 4.4.2磁場損耗　p64 4.5電感之等效模型　p65 第五章 改良式粒子群演算法於螺旋電感最佳化Q值實驗結果與分析　p68 5.1電感模型模擬　p68 5.2螺旋電感最佳化之模擬結果　p72 第六章 總結與未來展望　p77 6.1總結　p77 6.2未來展望　p78 參考文獻　p79
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指導教授	莊堯棠(Y.-T. Juang)	審核日期	2015-7-14
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