introduction and development of surrogate management framework for solving optimization problems
نویسندگان
چکیده
in this paper, we have outlined the surrogate management framework for optimization of expensive functions. an initial simple iterative method which we call the “strawman” method illustrates how surrogates can be incorporated into optimization to stand in for the most expensive function. these ideas are made rigorous by incorporating them into the framework of pattern search methods. the smf algorithm is presented, including mesh definition, and choice of polling points. in summarizing the ideas of surrogate-based optimization, we enrich this paper with an admittedly simplistic analogy which helps to compare optimization strategies.
منابع مشابه
INTRODUCTION AND DEVELOPMENT OF SURROGATE MANAGEMENT FRAMEWORK FOR SOLVING OPTIMIZATION PROBLEMS
In this paper, we have outlined the surrogate management framework for optimization of expensive functions. An initial simple iterative method which we call the “Strawman” method illustrates how surrogates can be incorporated into optimization to stand in for the most expensive function. These ideas are made rigorous by incorporating them into the framework of pattern search methods. The SMF al...
متن کاملAPPLICATION OF KRIGING METHOD IN SURROGATE MANAGEMENT FRAMEWORK FOR OPTIMIZATION PROBLEMS
In this paper, Kriging has been chosen as the method for surrogate construction. The basic idea behind Kriging is to use a weighted linear combination of known function values to predict a function value at a place where it is not known. Kriging attempts to determine the best combination of weights in order to minimize the error in the estimated function value. Because the actual function value...
متن کاملFOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems
These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this pa...
متن کاملAn Efficient and Safe Framework for Solving Optimization Problems
Interval methods have shown their ability to locate and prove the existence of a global optima in a safe and rigorous way. Unfortunately, these methods are rather slow. Efficient solvers for optimization problems are based on linear relaxations. However, the latter are unsafe, and thus may overestimate, or worst, underestimate the very global minima. This paper introducesQuadOpt, an efficient a...
متن کاملOptLets: A Generic Framework for Solving Arbitrary Optimization Problems
Meta-heuristics are an effective paradigm for solving large-scale combinatorial optimization problems. However, the development of such algorithms is often very time-consuming as they have to be designed for a concrete problem class with little or no opportunity for reuse. In this paper, we present a generic software framework that is able to handle different types of combinatorial optimization...
متن کاملA FAST GA-BASED METHOD FOR SOLVING TRUSS OPTIMIZATION PROBLEMS
Due to the complex structural issues and increasing number of design variables, a rather fast optimization algorithm to lead to a global swift convergence history without multiple attempts may be of major concern. Genetic Algorithm (GA) includes random numerical technique that is inspired by nature and is used to solve optimization problems. In this study, a novel GA method based on self-a...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
international journal of mathematical modelling and computationsجلد ۱، شماره ۴ (FALL)، صفحات ۲۳۵-۲۴۴
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023