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Sequential approximate optimization using dual subproblems based on incomplete series expansions
Structural and Multidisciplinary Optimization, 2008 In this paper, dual formulations for nonlinear multipoint approximations with diagonal approximate Hessian matrices are proposed; these approximations for example derive from the incomplete series expansion (ISE) proposed previously. A salient feature of
Albert A Groenwold +2 more
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Sequential approximation optimization assisted particle swarm optimization for expensive problems
Applied Soft Computing, 2019Abstract In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses
Xiwen Cai, Liang Gao 0001, Fan Li
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Sequential Approximate Optimization
2009In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by some analysis such as structural analysis, fluid mechanic analysis, and thermodynamic analysis.
Hirotaka Nakayama, Yeboon Yun, Min Yoon
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Approximately Optimal One-Parameter Boundaries for Group Sequential Trials
Biometrics, 1987We present a class of group sequential tests, indexed by a single parameter, that yields approximately optimal results. We also provide tables of key values to help in the design of group sequential tests that meet selected specifications.
Wang, Samuel K., Tsiatis, Anastasios A.
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Sequential Approximate Optimization in an NLP Filter Framework
11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006Nonlinear programming (NLP) filter methods are a recent development in trust region sequential quadratic programming (SQP) algorithms. The NLP filter is a bi-objective representation of the two competing aims to minimize the objective function and to satisfy the constraints.
L. F. Pascal Etman +2 more
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Sequential Approximate Optimization Using Kriging Metamodels
Transactions of the Korean Society of Mechanical Engineers A, 2005Nowadays, it is performed actively to optimize by using an approximate model. This is called the approximate optimization. In addition, the sequential approximate optimization (SAO) is the repetitive method to find an optimum by considering the convergence of an approximate optimum.
Yongshik Shin +3 more
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Multipoint Cubic Surrogate Functions for Sequential Approximate Optimization
9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2002Multipoint cubic approximations are investigated as surrogate functions for nonlinear objective and constraint functions in the context of sequential approximate optimization. The proposed surrogate functions match actual function and gradient values, including the current expansion point, thus satisfying the zero and first-order necessary conditions ...
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Sequential greedy approximation for certain convex optimization problems
IEEE Transactions on Information Theory, 2003Summary: A greedy algorithm for a class of convex optimization problems is presented. The algorithm is motivated from function approximation using a sparse combination of basis functions as well as some of its variants. We derive a bound on the rate of approximate minimization for this algorithm, and present examples of its application.
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Sequential approximate optimization using variable fidelity response surface approximations
Structural and Multidisciplinary Optimization, 2001The dimensionality and complexity of typical multidisciplinary systems hinders the use of formal optimization techniques in application to this class of problems. The use of approximations to represent the system design metrics and constraints has become vital for achieving good performance in many multidisciplinary design optimization (MDO) algorithms.
RODRIGUEZ MATAS, JOSE FELIX +3 more
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Improved Sequential Approximate Optimization for Aerodynamic Design Benchmark Problem
2019 IEEE Congress on Evolutionary Computation (CEC), 2019A modified surrogate-based optimization method based on Sequential Approximate Optimization (SAO) is proposed to purposively improve the efficiency of aerodynamic shape optimization. In this method, a specific initial sampling approach is proposed to obtain the initial sampling set of excellent properties of space-filling and orthogonality in the shape
Wenjie Wang +3 more
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