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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
is now available
-
Enumeration type parameters are now supported
-
New types of postprocessing tools (graphs and others) are now
available
-
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
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 have to the present time been thought
impracticable to target
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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George 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
Computación, 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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