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The Smart
Global Optimization Technology
Compact.Versatile.Cost-Effective
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The Leader of Smart Global 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 world-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 Integration 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,
training and the following services
- Consulting services
for design optimization
- System customization
and process integration
- Software 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 switch
between these two categories of solvers or use them simultaneously.
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 calculation 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 experience in CAE-based application, specially suitable for
CAE-based and FEA-Based Optimization.
- Real-world
industrial application and experience, practical and powerful
- Supported
by our consulting team, 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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