( en | de | ru )

 

  2. user
  3. @thorade
  4. open-source optimization

bookmarks  4

  •  

    We focus on scientific/engineering software development using RAD abilities of Python language, accompanied with free scientific libraries such as NumPy and SciPy. Our mainstream research activity is numerical optimization, including nonsmooth optimization and solving systems of nonlinear equations. OpenOpt framework - universal numerical optimization package with several own solvers (e.g. ralg) and connections to tens of other, graphical output of convergence and many other goodies FuncDesigner - tool to rapidly build functions over variables/arrays and get their derivatives via automatic differentiation. Also, you can perform integration, interpolation, solve systems of linear/nonlinear/ODE equations and numerical optimization problems coded in FuncDesigner by OpenOpt (see some examples in its doc), uncertainty analysis and interval analysis DerApproximator - tool to get (or check user-supplied) derivatives via finite-difference approximation SpaceFuncs - tool for 2D, 3D, N-dimensional geometric modeling with possibilities of parametrized calculations, numerical optimization and solving systems of geometrical equations
    14 years ago by @thorade
     
      Pythonmathematicsnumericalopen-sourceoptimizationsoftware
      (0)
      copydelete
       
       
    •  

      Evocosm is a set of classes that abstract the fundamental components of an evolutionary algorithm. I'll list the components here with a bit of introduction; you can review the details of the classes by downloading the code archives or by reviewing the online documentation (see the menu at the article's beginning for code and documentation links.) All class documentation was generated from source code comments using doxygen. These docs have not been thoroughly proofread, so they may contain a few typos and minor errors. Self-publishing has taught me the value of a good proofreader... ;} Evolutionary algorithms come in a variety of shapes and flavors, but at their core, they all share certain characteristics: populations that reproduce and mutate through a series of generations, producing future generations based on some measure of fitness. An amazing variety of algorithms can be built on that general framework, which leads me to construct a set of core classes as the basis for future applications.
      14 years ago by @thorade
       
        algorithmevolutionarynumericaloptimizationsoftwareopen-source
        (0)
        copydelete
         
         
      •  

        EvA2 (an Evolutionary Algorithms framework, revised version 2) is a comprehensive heuristic optimization framework with emphasis on Evolutionary Algorithms implemented in Java. It is a revised version of the JavaEvA optimization toolbox, which has been developed as a resumption of the former EvA software package. EvA2 integrates several derivation free optimization methods, preferably population based, such as Evolution Strategies (ES), Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), as well as classical techniques such as multi-start Hill Climbing or Simulated Annealing. Besides typical single-objective problems, multi-modal and multi-objective problem are handled directly by the EvA2 framework. Via the Java mechanism of Remote Method Invocation (RMI), the algorithms of EvA2 can be distributed over network nodes based on a client-server architecture. EvA2 aims at two groups of users. Firstly, the end user who does not know much about the theory of Evolutionary Algorithms, but wants to use Evolutionary Algorithms to solve an application problem. Secondly, the scientific user who wants to investigate the performance of different optimization algorithms or wants to compare the effect of alternative or specialized evolutionary or heuristic operators. The latter usually knows more about evolutionary algorithms or heuristic optimization and is able to extend EvA2 by adding specific optimization strategies or solution representations. EvA2 is being used as teaching aid in lecture tutorials, as a developing platform in student research projects and applied to numerous optimisation problems within active research and ongoing industrial cooperations.
        14 years ago by @thorade
         
          algorithmevolutionarynumericaloptimizationsoftwareopen-source
          (0)
          copydelete
           
           
        •  

          JModelica.org is an extensible Modelica-based open source platform for optimization, simulation and analysis of complex dynamic systems. The main objective of the project is to create an industrially viable open source platform for optimization of Modelica models, while offering a flexible platform serving as a virtual lab for algorithm development and research. As such, JModelica.org is intended to provide a platform for technology transfer where industrially relevant problems can inspire new research and where state of the art algorithms can be propagated form academia into industrial use. JModelica.org is a result of research at the Department of Automatic Control, Lund University, and is now maintained and developed by Modelon AB.
          15 years ago by @thorade
           
            Modelicamodellingoptimizationsimulationsoftwareopen-source
            (0)
            copydelete
             
             
          • ⟨⟨
          • ⟨
          • 1
          • ⟩
          • ⟩⟩

          publications  

            No matching posts.
          • ⟨⟨
          • ⟨
          • ⟩
          • ⟩⟩