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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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模擬退火系統於震測速度選取之最佳化之研究
(2010)
黃國源
;
HUANG KOU-YUAN
;
國立交通大學資訊工程學系（所）
我們提出一個運用模擬退火演算法來解決反射震測訊號處理中速度選取 (velocity picking) 最佳化的問題。速度分析是基於在一個2D的速度影像 (semblance image) 裡挑選出time-velocity pairs，time-velocity pairs形成的曲線 (polyline) 表示速度與時間之間的關係函式。傳統的震測訊號速度選取是由有經驗的地球物理專家透過觀察semblance image及seismic survey來進行選取，需要花費不少時間。我們提出整個速度選取問題可以被公式化成為最佳化問題。我們使用peak-climbing clustering來識別semblance中極大peaks之所在。在peaks排列集合而成的有限解集合中，利用模擬退火演算法找到全域最佳解。從實驗中，使用模擬震測資料以模擬退火演算法找到最佳化的stacking velocity與時間的對應關係，再作normal moveout correction (NMO) 修正及stacking，得出stacked traces，可以反映真實地層的原貌 (in time)。我們的研究結果將有助於速度選取最佳化及自動化，並有助於震測訊號的解釋。
模擬退火與基因演算於震測速度選取之最佳化之研究
(2011)
黃國源
;
HUANG KOU-YUAN
;
國立交通大學資訊工程學系（所）
我們採用模擬退火與基因演算法來解決反射震測訊號中速度分析的問題。模擬退火與基因演算法具有搜尋全域最佳解的能力。傳統的震測速度選取是由地球物理專家透過觀察 semblance image 來進行，得出時間與速度之間的關係函式，然而此過程耗時。我們把整個速度選取問題轉化成為組合最佳化的問題，並建立能量函式，包含被選取點的semblance、數量、與interval velocity和velocity slope的限制條件，計算候選的time-velocity pairs的能量，進而利用模擬退火演算法與基因演算法找到全域最佳解，即最佳化的 stacking velocity與時間的對應關係。之後，可進一步執行動態修正及共中點疊加，以反映真實地層的原貌 (in time)。我們的研究有助於進一步的震測資料處理與解釋。
An approximate approach of global optimization for polynomial programming problems
(1998-06-16)
Li, HL
;
Chang, CT
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Many methods for solving polynomial programming problems can only find locally optimal solutions. This paper proposes a method for finding the approximately globally optimal solutions of polynomial programs. Representing a bounded continuous variable x(i) as the addition of a discrete variable d(i) and a small variable epsilon(i), a polynomial term x(i)x(j) can be expanded as the sum of d(i)x(j), d(j) epsilon(i) and epsilon(i) epsilon(j). A procedure is then developed to fully linearize d(i)x(j) and d(j) epsilon(i), and to approximately linearize epsilon(i) epsilon(j) with an error below a pre-specified tolerance. This linearization procedure can also be extended to higher order polynomial programs. Several polynomial programming examples in the literature are tested to demonstrate that the proposed method can systematically solve these examples to find the global optimum within a pre-specified error. (C) 1998 Elsevier Science B.V.
An interval method for computing the stability margin of real uncertainty problems
(2000-07-01)
Ge, ZM
;
Chu, LW
;
機械工程學系
;
Department of Mechanical Engineering
Frequently, in practical control system design, some designing parameters are uncertain. These uncertain parameters may vary with temperature, humidity or other environmental variable, and these variations will have an impact on the stability of the system. In this paper, we use a global optimal method-interval method, by which stability margins of these uncertain parameters can be computed. Copyright (C) 2000 John Wiley & Sons, Ltd.
A robust evolutionary algorithm for global optimization
(2002)
Yang, JM
;
Lin, CJ
;
Kao, CY
;
生物科技學系
;
生物資訊及系統生物研究所
This paper studies an evolutionary algorithm for global optimization. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing-based mutations and self-adaptive mutations. The proposed approach is experimentally analyzed by showing that its components can integrate with one another and possess good local and global properties. Following the description of implementation details, the approach is then applied to several widely used test sets, including problems from international contests on evolutionary optimization. Numerical results indicate that the new approach performs very robustly and is competitive with other well-known evolutionary algorithms.
Approximately global optimization for assortment problems using piecewise linearization techniques
(2002-08-01)
Li, HL
;
Chang, CT
;
Tsai, JF
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recently, Li and Chang proposed an approximate model for assortment problems. Although their model is quite promising to find approximately global solution, too many 0-1 variables are required in their solution process. This paper proposes another way for solving the same problem. The proposed method uses iteratively a technique of piecewise linearization of the quadratic objective function. Numerical examples demonstrate that the proposed method is computationally more efficient than the Li and Chang method. (C) 2002 Elsevier Science B.V. All rights reserved.
Global optimization for signomial discrete programming problems in engineering design
(2002-12-01)
Tsai, JF
;
Li, HL
;
Hu, NZ
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This paper proposes a novel method to solve signomial discrete programming (SDP) problems frequently occurring in engineering design. Various signomial terms are first convexified following different strategies. The! original SDP program is then converted into a convex integer program solvable by commercialized packages to obtain globally optimal solutions. Compared with current SDP methods, the proposed method is guaranteed to converge to a global optimum, is computationally more efficient, and is capable of treating zero boundary problems. Numerical examples are presented to demonstrate the usefulness of the proposed method in engineering design.
Treating free variables in generalized geometric global optimization programs
(2005-09-01)
Li, HL
;
Tsai, JF
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Generalized geometric programming (GGP) problems occur frequently in engineering design and management. Recently, some exponential-based decomposition methods [Maranas and Floudas, 1997,Computers and Chemical Engineering 21(4), 351-370; Floudas et al., 1999 , Handbook of Test Problems in Local and Global Optimization, Kluwer Academic Publishers, Boston, pp. 5-105; Floudas, 2000 Deterministic Global Optimizaion: Theory, Methods and Application, Kluwer Academic Publishers, Boston, pp. 257-306] have been developed for GGP problems. These methods can only handle problems with positive variables, and are incapable of solving more general GGP problems. This study proposes a technique for treating free (i.e., positive, zero or negative) variables in GGP problems. Computationally effective convexification rules are also provided for signomial terms with three variables.
Technical note - On optimization approach for multidisk vertical allocation problems
(2005-09-16)
Tsai, JF
;
Li, HL
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Multidisk vertical allocation (MDVA) problems intend to find an allocation of relations to disks such that the expected query cost is minimized. Recently, Chang [European Journal of Operational Research 143 (2002) 210] modified Rotem et al.'s [IEEE Transactions on Knowledge and Data Engineering 5 (1993) 882] method for solving an MDVA problem using a smaller number of binary variables. Chang's method however is unable to treat MDVA problems with possible replication of relations. This paper proposes another method to solve MDVA problems, which is more effective than Rotem et al.'s and is able to treat replication problems insolvable by Chang's method. (c) 2004 Elsevier B.V. All rights reserved.
A global optimization method for packing problems
(2006-09-01)
Tsai, JF
;
Li, HL
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The objective of packing problems is to determine an optimal way of placing a given set of three-dimensional (3D) rectangular cartons within a minimum volume 3D rectangular container. Current packing optimization methods either use too many extra 0-1 variables or find it difficult to obtain a globally optimal solution. This study proposes an efficient method for finding the global optimum of packing problems. First the traditional packing optimization problem is converted into an equivalent program containing many fewer 0-1 variables than contained in current methods. Then the global optimum of the converted program is found by utilizing piecewise linearization techniques. The numerical examples demonstrate that the proposed method is capable of finding the global optimum of a packing problem.

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