A Hybrid Optimization Strategy Using Design-Space Evolution and POD-based Order Reduction

2012 ◽  
Author(s):  
Satyajit Ghoman ◽  
Darius Sarhaddi ◽  
Ping Chen ◽  
Zhicun Wang ◽  
Rakesh Kapania
Keyword(s):  
Design Space ◽  
Order Reduction ◽  
Hybrid Optimization ◽  
Optimization Strategy

Hybrid optimization strategy beyond local optima in aerospace panel designs

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 588-593
Author(s):  
K. L. Chan ◽  
David Kennedy ◽  
Fred W. Williams
Keyword(s):  
Hybrid Optimization ◽  
Optimization Strategy ◽  
Local Optima

scholarly journals An Efficient Hybrid Optimization Strategy for Surface Reconstruction

2021 ◽  
Author(s):  
Giulia Bertolino ◽  
Marco Montemurro ◽  
Nicolas Perry ◽  
Franck Pourroy
Keyword(s):  
Surface Reconstruction ◽  
Hybrid Optimization ◽  
Optimization Strategy

scholarly journals Design Space Approach for Preservative System Optimization of an Anti-Aging Eye Fluid Emulsion

2015 ◽  
Vol 18 (3) ◽  
pp. 551 ◽  
Author(s):  
Felipe Rebello Lourenço ◽  
Fabiane Lacerda Francisco ◽  
Márcia Regina Spuri Ferreira ◽  
Terezinha De Jesus Andreoli ◽  
Raimar Löbenberg ◽  
...  
Keyword(s):  
Design Space ◽  
Challenge Test ◽  
System Optimization ◽  
Effective Concentration ◽  
Optimization Strategy ◽  
Cosmetic Products ◽  
Cosmetic Product ◽  
Space Design ◽  
New Formula ◽  
Space Approach

The use of preservatives must be optimized in order to ensure the efficacy of an antimicrobial system as well as the product safety. Despite the wide variety of preservatives, the synergistic or antagonistic effects of their combinations are not well established and it is still an issue in the development of pharmaceutical and cosmetic products. The purpose of this paper was to establish a space design using a simplex-centroid approach to achieve the lowest effective concentration of 3 preservatives (methylparaben, propylparaben, and imidazolidinyl urea) and EDTA for an emulsion cosmetic product. Twenty-two formulae of emulsion differing only by imidazolidinyl urea (A: 0.00 to 0.30% w/w), methylparaben (B: 0.00 to 0.20% w/w), propylparaben (C: 0.00 to 0.10% w/w) and EDTA (D: 0.00 to 0.10% w/w) concentrations were prepared. They were tested alone and in binary, ternary and quaternary combinations. Aliquots of these formulae were inoculated with several microorganisms. An electrochemical method was used to determine microbial burden immediately after inoculation and after 2, 4, 8, 12, 24, 48, and 168 h. An optimization strategy was used to obtain the concentrations of preservatives and EDTA resulting in a most effective preservative system of all microorganisms simultaneously. The use of preservatives and EDTA in combination has the advantage of exhibiting a potential synergistic effect against a wider spectrum of microorganisms. Based on graphic and optimization strategies, we proposed a new formula containing a quaternary combination (A: 55%; B: 30%; C: 5% and D: 10% w/w), which complies with the specification of a conventional challenge test. A design space approach was successfully employed in the optimization of concentrations of preservatives and EDTA in an emulsion cosmetic product. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

Derivative-Free Hybrid Optimization Method for Top Design of Satellite System

2011 ◽  
Vol 308-310 ◽  
pp. 2413-2417
Author(s):  
Ying Guo Chen ◽  
Shuai Lu ◽  
Xiao Lu Liu ◽  
Ying Wu Chen
Keyword(s):  
Optimization Algorithm ◽  
Gaussian Processes ◽  
Design Space ◽  
Optimization Method ◽  
Satellite System ◽  
Pattern Search ◽  
Hybrid Optimization ◽  
Hybrid Optimization Algorithm ◽  
Hybrid Optimization Method ◽  
Derivative Free

This paper combines a derivative-free hybrid optimization algorithm, generalize pattern search (GPS), with Treed Gaussian Processes (TGP) to create a new hybrid optimization algorithm. The goal is to use the method for top design of satellite system, in which the objective or constraint functions usually are computationally expensive black-box functions. TGP model partitions the design space into disjoint regions, and employs independent Gaussian Processes (GP) in each partition to represent the time consumption of true problem responses. Utilizing the TGP, we generate the new “promising” points, which are the combination of model-predicted values and estimated model errors. Then, these points are used to guide GPS search in the design space efficiently. The hybrid optimization method is applied to top design of multi-satellites cooperated observation. The results demonstrate that the proposed method can not only increase the chance of obtaining optimal solution but also cut down the cost of function evaluations.

Tuning a Hybrid Optimization Algorithm by Determining the Modality of the Design Space

2001 ◽  
Author(s):  
Kurt Hacker ◽  
John Eddy ◽  
Kemper Lewis
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Optimization Algorithm ◽  
Design Space ◽  
Hybrid Optimization ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Hybrid Optimization Algorithm ◽  
Linear Programming Algorithm ◽  
Algorithm Benchmarking

Abstract In this paper we present an approach for increasing the efficiency of a hybrid Genetic/Sequential Linear Programming algorithm. We introduce two metrics for evaluating the modality of the design space and then use this information to efficiently switch between the Genetic Algorithm and SLP algorithm. The motivation for this study is an effort to reduce the computational expense associated with the use of a Genetic Algorithm by reducing the number of function evaluations needed to find good solutions. In the paper the two metrics used to evaluate the modality of the design space are the variance in fitness of the population of the designs in the Genetic Algorithm and the error associated with fitting a response surface to the designs evaluates by the Genetic Algorithm. The effectiveness of this approach is demonstrated by considering a highly multimodal Genetic Algorithm benchmarking problem.

scholarly journals Zika Virus Prediction Using AI-Driven Technology and Hybrid Optimization Algorithm in Healthcare

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Pankaj Dadheech ◽  
Abolfazl Mehbodniya ◽  
Shivam Tiwari ◽  
Sarvesh Kumar ◽  
Pooja Singh ◽  
...  
Keyword(s):  
Public Health ◽  
Optimization Algorithm ◽  
Zika Virus ◽  
Health Hazard ◽  
Data Encryption ◽  
Machine Learning Algorithms ◽  
Hybrid Optimization ◽  
Infection Frequency ◽  
Optimization Strategy ◽  
Hybrid Optimization Algorithm

The Zika virus presents an extraordinary public health hazard after spreading from Brazil to the Americas. In the absence of credible forecasts of the outbreak's geographic scope and infection frequency, international public health agencies were unable to plan and allocate surveillance resources efficiently. An RNA test will be done on the subjects if they are found to be infected with Zika virus. By training the specified characteristics, the suggested Hybrid Optimization Algorithm such as multilayer perceptron with probabilistic optimization strategy gives forth a greater accuracy rate. The MATLAB program incorporates numerous machine learning algorithms and artificial intelligence methodologies. It reduces forecast time while retaining excellent accuracy. The projected classes are encrypted and sent to patients. The Advanced Encryption Standard (AES) and TRIPLE Data Encryption Standard (TEDS) are combined to make this possible (DES). The experimental outcomes improve the accuracy of patient results communication. Cryptosystem processing acquires minimal timing of 0.15 s with 91.25 percent accuracy.

Sign in / Sign up

Export Citation Format

Share Document