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Universal software for real-life optimization tasks |
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IOSO |
Now our optimization technology is
offered as a Full-box Software Package with the elaborated and
user friendly interface including pre and post processing and
English help.
Third-party references to
IOSO... |
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Multi-objective Optimization Software IOSO 2.0 |
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Main new features and improvements of IOSO 2.0 |
- IOSO PM - unique parallel algorithm of optimization
is now available
- IOSO now supports parallel calculations on Windows
HPC clusters
- Direct integration with
Concepts NREC TurbooptII software is now
available
- Direct integration with
SolidWorks 2009 and
SolidWorks 2010 are now available
- Enumeration type parameters are now supported
- Enumeration type parameters are now supported
- New types of built-in functions (like Abs, Min, Max)
enhancing possibilities of Synthetic parameters are now
available
- New integration examples with
FlowVision CFD software are now included
- Procedures of project settings and the forms of
result tables were sufficiently improved
- Feature of saving of all result files of different
applications for Paretto-optimal points is available
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Distinctive features of IOSO optimization technology: |
- multiobjective optimization for large-dimensionality
problems (up to 100 independent design variables and up to
100 constraints), which allows to reach the increase of
efficiency up to 7 times higher than that of
middle-dimensionality optimization tasks (20...40 design
variables)
- low expenditures for optimal solution search (reduction
of the number of analysis code direct calls calls up to 20
times in comparison with traditional approaches and genetic
algorithms (GA), depending on the complexity and
dimensionality of the task)
- full automatic optimization technology algorithms with
easy to use procedure of task setting
- the possibility to solve multidisciplinary optimization
problems
- multiobjective optimization for stochastic problems,
having complex topology of objective and the large number of
constraints. Now it is well-known that many methods are
capable of solving the tasks having up to 10 - 20 variables,
and it is not known the analogues to IOSO optimizer that is
designed for large-dimensional multiobjective tasks
- solving all classes of optimization problems including
stochastic, multiextreme and having non-differential
peculiarities
- Maximum use of the potential of multiprocessor systems
and local area networks for reduce total time of solving
optimization task
- Efficient use of difficult-to-parallelize applications
and computation models
- Solution of complex problems which
More about IOSO NM
IOSO NM base information (PDF, 800kB)
IOSO PM base information (PDF, 890kB)..
"Look and feel" IOSO
presentation with audio-voice support (download)...
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Single-objective Optimization Software IOSO NS GT 2.0 |
IOSO NS GT v.2.0 is the program
package implementing IOSO Technology algorithms for a
single-objective nonlinear optimization with a moderate number
of design variables (up to 100).
High efficiency of the evolutionary self-organizing algorithm.
The efficiency is guaranteed by internal adaptive choice of
the algorithm suitable for each particular problem. This
feature results in solving complex optimization problems with
minimal number of evaluations of the system mathematical
model.
More about IOSO NS GT 2.0
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| IOSO
software |
Easily integrating software
for solving all spectrum of optimization tasks.
IOSO Software is based on open architecture and therefore is
compatible with almost all CAM/CAD/CAE applications both
commercial and in-house.
There are different independent versions of our Software
designed for solving the following classes of nonlinear
optimization tasks.
All IOSO-based software packages were developed according to
the common concept of the optimization task statement
including initial data settings, data exchange with user's
applications, and the analysis of the obtained results.
Details.. |
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Third-party references to IOSO |
- mGeorge S. Dulikravich, Florida International University
AFOSR - Air Force Office of Scientific Research, US:
- REPORT DOCUMENTATION PAGE
Grant Title "Hybrid Robust Multi-Objective Evolutionary
Optimization Algorithm", page 2
- "... Currently, a Russian commercially available
software named IOSO is the most efficient and the most
robust multi-objective optimization software... IOSO, which
involves concepts of neural networks, radial basis
functions, and self-adapting response surface methodologies,
requires the minimum number of the objective function
evaluations and that is the most versatile and robust
multi-objective optimizer..." Details...
- Timothy W. Simpson, The Pennsylvania State University,
US
Vasilli Toropov, University of Leeds, UK
Vladimir Balabanov, The Boeing Company, Seattle, USA
- Felipe A. C. Viana, University of Florida, Gainesville,
USA
F.A.C. (2008) Design and Analysis of Computer Experiments in
Multidisciplinary Design Optimization: A Review of How Far
We Have Come - or Not, 12th AIAA/ISSMO Multidisciplinary
Analysis and Optimization Conference, Victoria, British
Columbia, Canada, AIAA, AIAA-2008-5802, page 13
- "... IOSO offers unique state of the art optimization
algorithms that are based on self-organizational strategy
and efficiently combine traditional response surface
methodology with gradient-based optimization and
evolutionary algorithms in a single run. The offered
algorithms are equally efficient for the problems of complex
and simple topology that may include mixed types of
variables..." Details...
- Carlos A. Coello Coello and Ricardo Landa Becerra
Evolutionary Computation Group Departamento de Computacion,
Mexico
- Evolutionary Multiobjective Optimization in Materials
Science and Engineering, Materials and Manufacturing
Processes, Volume 24, Issue 2 February 2009 , pages 119 -
129
- 6.5 Design of alloys
"... IOSO consists of two stages. In the first stage, an
approximate model of the objective functions is created. In
the second stage, this approximate model is optimized. IOSO
incorporates evolutionary algorithms, and artificial neural
networks with radial basis functions that are used to build
the response surfaces. The idea is to use this metamodel (or
approximate model) to perform a very reduced number of
evaluations of the actual objective functions of the
problem..." Details...
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