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The Smart
Global Optimization Technology
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The Leader of Smart Gloabl Optimization Technology
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SmartDO focuses
on the practical global optimization technology. Either with the
Gradient-Based NLP or the Genetic Algorithms, SmartDO has been applied
on may different industrial application.
The worldi-leading Response Smoothing Technology
in SmartDO allows the users to search global optimum
with the gradient-based approach. Additionally, SmartDO can
eliminate the numerical noise caused by meshing, discretization,
and other phenomena during numerical analysis.
The Robust Genetic
Algorithms in SmartDO is powered by the technologies
of Adaptive Penalty Function, Automatic Schema Representation, Automatic
Population and Generation Number Calculation, Adaptive and Automatic
Cross-Over Probability Calculation, Absolute Descent. This makes
SmartDO much more powerful and efficient than other packages on the
market.
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Engineering Lifecycle Integration
The open architecture of Tcl/Tk in SmartDO make
the Engineering CAx Lifecycle Intergatrion possible. Through the building
of CAx Cycle Integration, SmartDO can reduce the human error during the CAx
design cycle, and further parameterize, automate and optimize the design
process.
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SmartDO : A Smart Design Optimization System and Service
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FEA-Opt
Technology provides total solutions of design optimization to the
customers, based on our SmartDO technology. We provide software,
trianing and the following services
- Consulting services
for design optimizaiton
- System customization
and process integration
- Softwareware integration
and ODM for numerical optimization techniques
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With
Gradient-Based RCFDM, the Robust Genetic Algorithms and the Smart
Particle Swarm
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With both
Gradient-Based NLP and the Genetic Algorithms, you can
swith between these two catagories of solvers or use them simulteniously.
The Genetic Algorithms can be used to solve problems
not suitable for the gradient-based solver. And the Gradient-Based
NLP solver provide a more efficient and accurate way of getting
the optimum. These two algorithms together make SmartDO a practical
and powerful optimizer.
The Smart Particle Swarm is a solver
with the benefits of both the Robust Genetic Algorithms and the Gradient-Based
approaches. While it starts the global optimization with multiple initial
design points, each individual design (which is called the "particle") will
exchange design history and "experience" with each other. What is unique
is, the calculation of the new search direction is a semi-gradient approach.
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Concurrent Sizing, Shaping and Topology Optimization
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Because there
are various types of design variables available in the Robust Genetic
Algorithms of SmartDO, the users can perform Concurrent
Sizing, Shaping and Topology Optimization in a stable and efficient
fashion.
The problem of Concurrent Sizing, Shaping and
Topology Optimization usually requires intensive numerical effort.
With the Smart Heuristic Search Technology,
SmartDO is able to avoid unnecessary caclulation and save considerable
amount of computational time.
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Specially Developed for CAE-Based Application, with Many Successful
Examples
Due to the
unique development background of SmartDO, it have certain advantage
over other packages
- Based on decades
of experinec in CAE-based application, specially suitable for CAE-based
and FEA-Based Optimization.
- Real-world
industrial applicaiton and experience, practical and pwoerful
- Supported
by our consulting teacm, focusing directly on the key problems
Our customers
have successfully coupled SmartDO with many different CAE/FEA/CFD
packages, like ADINA, ANSYS, ABAQUS, CFX, Fluent, SolidEdge......
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Currently, we
have helped many of our customers achieving outstanding results and
building up powerful systems using SmartDO. For details of our products
and services, please also see our brochure.
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All brand or product names are trademarks
or registered trademarks of their
respective holders. Copyright of all materials
in the links belongs to their respective authors.
I am not responsible for any contents inside any links.
c)Copyright, 1998-, Shen-Yeh
Chen, Ph.D. All rights reserved.陳申岳
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