Topic
Shekel function
About: Shekel function is a research topic. Over the lifetime, 2 publications have been published within this topic receiving 60 citations.
Papers
TL;DR: A highly efficient Monte Carlo algorithm for global optimization has been developed which accepts beneficial moves, rejects all detrimental ones, picks a new step size at random from a guided range, and samples a new region of the response surface using a randomly generated directional search technique.
Abstract: A highly efficient Monte Carlo algorithm for global optimization has been developed which accepts beneficial moves, rejects all detrimental ones, picks a new step size at random from a guided range, and samples a new region of the response surface using a randomly generated directional search technique. The step size guide is the modified Heaviside function: r = r0 0 3nn), where m is the number of frustrated steps and nn a decision parameter. This approach quickly steps through local optima. Two strategies have been evolved leading to solutions for both the highly intractable modified n-dimensional shekel function and COSn, the “cosine function”. The strategies are efficient: starting from the point (10,10), an average of only 187 steps (successful and otherwise) taking a total of 0.22 shekels of time were required to find the minimum of the COS2 function to three significant figures with a success rate of 987/1000. In the case ...
3 citations
6 Sep 2006
TL;DR: Two popular techniques, second order regression and kriging, along with a new commercial application called Datascape are compared on model accuracy, computational e‐ciency, robustness, transparency, and ease of use.
Abstract: Using surrogate models in place of high fldelity engineering simulations can help reduce design cycle times and cost by enabling rapid analysis of alternative designs Surrogate models can also be used in a deliverable product as an e‐cient replacement for large lookup tables or as a soft sensor to predict quantities than cannot be directly measured Many difierent surrogate modeling techniques exist, including new commercial technologies, each with difierent capabilities and pitfalls The goal of this research is to aid the designer in selecting the appropriate surrogate model by comparing two popular techniques, second order regression and kriging, along with a new commercial application called Datascape The three difierent modeling techniques are compared on model accuracy, computational e‐ciency, robustness, transparency, and ease of use The comparisons were done using three test problems: an Earth-Mars transfer orbit problem, the analytic Shekel function, and a low Earth orbit three-satellite constellation design problem It was found that kriging models performed the best when the sample data used to build the models was sparse, when larger sample sets were used Datascape produced more accurate models
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2006 | 1 |
| 1998 | 1 |