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High efficiency IOSO design optimization software


The universal software for quick and easy solution to real-life optimization tasks.

IOSO optimization technology is offered as a Full-box Software Package with the elaborated and easy user friendly interface including pre and post processing, help.

Third-party references to IOSO...


To Get a trial version of IOSO optimization software...


  Multi-objective Optimization Software IOSO 3

  Main new features and improvements of IOSO 3

  • The ability to multi-user and multi-tasking program mode

  • Direct integration with Creo

  • New types of postprocessing tools (graphs and others) are now available

  • New feature of saving of all result files of different applications for Paretto-optimal points is available

 Main new features and improvements of IOSO 2.3

  • IOSO PM - unique parallel algorithm of optimization is now available

  • Direct integration with Concepts NREC Turboopt II,  ANSYS WB2,  FLOW-3D,  SolidWorks, FlowVision

  • Enumeration type parameters are now supported

  • New types of built-in functions (like Abs, Min, Max) enhancing possibilities of Synthetic parameters are now available

  • Procedures of project settings and the forms of result tables were sufficiently improved

    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)...

   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

    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..

   Third-party references to IOSO

George S. Dulikravich, Florida International University
AFOSR - Air Force Office of Scientific Research, US:

Grant TitleHybrid 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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