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

Hybrid Optimization Strategy Beyond Local Optima in Aerospace Panel Designs

AIAA Journal ◽  
10.2514/2.777 ◽  
1999 ◽  
Vol 37 (5) ◽  
pp. 588-593 ◽  
Author(s):  
K. Leung 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

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

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.

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