Thesis--Wisconsin. / Includes bibliographical references (leaves 101-102).

Thesis (Ph.D.)--University of Iowa, 2006. / Supervisor: Jasbir S. Arora. Includes bibliographical references (leaves 177-188).

Thesis (Ph. D.)--University of Louisville, 2009. / Department of Electrical and Computer Engineering. Vita. "December 2009." Includes bibliographical references (leaves 108-114).

Thesis (Ph. D.)--University of Glasgow, 1998. / Print version also available.

Leung, Ka Lun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 118-126). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction and Global Constraints --- p.3 / Chapter 1.2 --- Soft Constraints --- p.4 / Chapter 1.3 --- Motivation and Goal --- p.5 / Chapter 1.4 --- Outline of the Thesis --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.8 / Chapter 2.1.1 --- Backtracking Tree Search --- p.10 / Chapter 2.1.2 --- Local Consistency in CSP --- p.11 / Chapter 2.1.3 --- Constraint Optimization Problem --- p.16 / Chapter 2.2 --- Weighted Constraint Satisfaction --- p.21 / Chapter 2.2.1 --- Branch and Bound Search --- p.23 / Chapter 2.2.2 --- Local Consistency in WCSP --- p.26 / Chapter 2.3 --- Global Constraints --- p.35 / Chapter 2.4 --- Flow Theory --- p.37 / Chapter 3 --- Related Work --- p.39 / Chapter 3.1 --- Handling Soft Constraints in COPs --- p.39 / Chapter 3.2 --- Global Constraints --- p.40 / Chapter 3.2.1 --- Hard Global Constraints --- p.40 / Chapter 3.2.2 --- Soft Global Constraints --- p.41 / Chapter 3.3 --- Local Consistency in Weighted CSP --- p.42 / Chapter 4 --- “Soft as Hard´ح Approach --- p.44 / Chapter 4.1 --- The General “Soft as Hard´ح Approach --- p.44 / Chapter 4.2 --- Cost-based GAC --- p.49 / Chapter 4.3 --- Empirical Results --- p.53 / Chapter 5 --- Weighted CSP Approach --- p.55 / Chapter 5.1 --- Strong 0-Inverse Consistency --- p.55 / Chapter 5.1.1 --- 0-Inverse Consistency and Strong 0-Inverse Consistency --- p.56 / Chapter 5.1.2 --- Comparison with Other Consistencies --- p.62 / Chapter 5.2 --- Generalized Arc Consistency Star --- p.65 / Chapter 5.3 --- Full Directional Generalized Arc Consistency Star --- p.72 / Chapter 5.4 --- Generalizing EDAC* --- p.78 / Chapter 5.5 --- Implementation Issues --- p.87 / Chapter 6 --- Towards A Library of Efficient Soft Global Constraints --- p.90 / Chapter 6.1 --- The allDifferent Constraint --- p.91 / Chapter 6.1.1 --- All Interval Series --- p.93 / Chapter 6.1.2 --- Latin Square --- p.95 / Chapter 6.2 --- The GCC Constraint --- p.97 / Chapter 6.2.1 --- Latin Square --- p.100 / Chapter 6.2.2 --- Round Robin Tournament --- p.100 / Chapter 6.3 --- The Same Constraint --- p.102 / Chapter 6.3.1 --- Fair Scheduling --- p.104 / Chapter 6.3.2 --- People-Mission Scheduling --- p.105 / Chapter 6.4 --- The Regular Constraint --- p.106 / Chapter 6.4.1 --- Nurse Rostering Problem --- p.110 / Chapter 6.4.2 --- Modelling Stretch() Constraint --- p.111 / Chapter 6.5 --- Discussion --- p.113 / Chapter 7 --- Conclusion and Remarks --- p.115 / Chapter 7.1 --- Contributions --- p.115 / Chapter 7.2 --- Future Work --- p.117 / Bibliography --- p.118

Meta-heuristics have already been used extensively for the successful solution of a wide range of real world problems. A few industrial engineering examples include inventory optimization, production scheduling, and vehicle routing, all areas which have a significant impact on the economic success of society. Unfortunately, it is not always easy to predict which meta-heuristic from the multitude of algorithms available, will be best to address a specific problem. Furthermore, many algorithm development options exist with regards to operator selection and parameter setting. Within this context, the idea of working towards a higher level of automation in algorithm design was born. Hyper-heuristics promote the design of more generally applicable search methodologies and tend to focus on performing relatively well on a large set of different types of problems. This thesis develops a heterogeneous meta-hyper-heuristic algorithm (HMHH) for single-objective unconstrained continuous optimization problems. The algorithm development process focused on investigating the use of meta-heuristics as low level heuristics in a hyper-heuristic context. This strategy is in stark contrast to the problem-specific low level heuristics traditionally employed in a hyper-heuristic framework. Alternative low level meta-heuristics, entity-to-algorithm allocation strategies, and strategies for incorporating local search into the HMHH algorithm were evaluated to obtain an algorithm which performs well against both its constituent low level meta-heuristics and four state- of-the-art multi-method algorithms. Finally, the impact of diversity management on the HMHH algorithm was investigated. Hyper-heuristics lend themselves to two types of diversity management, namely solution space diversity (SSD) management and heuristic space diversity (HSD) management. The concept of heuristic space diversity was introduced and a quantitative metric was defined to measure heuristic space diversity. An empirical evaluation of various solution space diversity and heuristic space diversity intervention mechanisms showed that the systematic control of heuristic space diversity has a significant impact on hyper-heuristic performance. / Thesis (PhD)--University of Pretoria, 2015. / Industrial and Systems Engineering / Unrestricted

No description available.

Various kinds of optimization problems involve nonlinear functions of binary variables that exhibit a property of diminishing marginal returns. Such a property is known as submodularity. Vast amount of work has been devoted to the problem of submodular optimization. In this thesis, we exploit structural information for several classes of submodular optimization problems. We strive for polynomial time algorithms with improved approximation ratio and strong mixed-integer linear formulations of mixed-integer non-linear programs where the epigraph and hypograph of submodular functions of a specific form appear as a substructure together with other side constraints. In Chapter 2, we develop approximation algorithms for the expected utility knapsack problem. We use the sample average approximation framework to approximate the stochastic problem as a deterministic knapsack-constrained submodular maximization problem, and then use an approximation algorithm to solve the deterministic counterpart. We show that a polynomial number of samples are enough for a deterministic approximation that is close in relative error. Then, exploiting the strict monotonicity of typical utility functions, we present an algorithm that maximizes an increasing submodular function over a knapsack constraint with approximation ratio better than the classical $(1-1/e)$ ratio. In Chapter 3, we present polyhedral results for the expected utility knapsack problem. We study a mixed-integer nonlinear set that is the hypograph of $f(a'x)$ together together with a knapsack constraint. We propose a family of inequalities for the convex hull of the nonlinear set by exploiting both the structure of the submodular function $f(a'x)$ and the knapsack constraint. Effectiveness of the proposed inequalities is shown by computational experiments on expected utility maximization problem with budget constraint using a branch-and-cut framework. In Chapter 4, we study a mixed-integer nonlinear set that is the epigraph of $f(a'x)$ together with a cardinality constraint. This mixed-integer nonlinear set arises as a substructure in various constrained submodular minimization problems. We develop a strong linear formulation of the convex hull of the nonlinear set by exploiting both the submodularity of $f(a'x)$ and the cardinality constraint. We provide a full description of the convex hull of the nonlinear set when the vector a has identical components. We also develop a family of facet-defining inequalities when the vector a has nonidentical components. We demonstrate the effectiveness of the proposed inequalities by solving mean-risk knapsack problems using a branch-and-cut framework.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 111-118). Also available via the Internet.

A global/local framework for multidisciplinary optimization of generalized aircraft wing structure has been developed. The concept of curvilinear stiffening members (spars, ribs and stiffeners) has been applied in the optimization of a wing structure. A global wing optimization framework EBF3WingOpt, which integrates the static aeroelastic, flutter and buckling analysis, has been implemented for exploiting the optimal design at the wing level. The wing internal structure is optimized using curvilinear spars and ribs (SpaRibs). A two-step optimization approach, which consists of topology optimization with shape design variables and size optimization with thickness design variables, is implemented in EBF3WingOpt. A local panel optimization EBF3PanelOpt, which includes stress and buckling evaluation criteria, is performed to optimize the local panels bordered by spars and ribs for further structural weight saving. The local panel model is extracted from the global finite element model. The boundary conditions are defined on the edges of local panels using the displacement fields obtained from the global model analysis. The local panels are optimized to satisfy stress and buckling constraints. Stiffened panel with curvilinear stiffeners is implemented in EBF3PanelOpt to improve the buckling resistance of the local panels. The optimization of stiffened panels has been studied and integrated in the local panel optimization. EBF3WingOpt has been applied for the optimization of the wing structure of the Boeing N+2 supersonic transport wing and NASA common research model (CRM). The optimization results have shown the advantage of curvilinear spars and ribs concept. The local panel optimization EBF3PanelOpt is performed for the NASA CRM wing. The global-local optimization framework EBF3GLWingOpt, which incorporates global wing optimization module EBF3WingOpt and local panel optimization module EBF3PanelOpt, is developed using MATLAB and Python programming to integrate several commercial software: MSC.PATRAN for pre and post processing, MSC.NASTRAN for finite element analysis. An approximate optimization method is developed for the stiffened panel optimization so as to reduce the computational cost. The integrated global-local optimization approach has been applied to subsonic NASA common research model (CRM) wing which proves the methodology's application scaling with medium fidelity FEM analysis. Both the global wing design variables and local panel design variables are optimized to minimize the wing weight at an acceptable computational cost. / Ph. D.