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 // Created on: 2014-07-18
 // Created by: Alexander Malyshev
 // Copyright (c) 2014-2014 OPEN CASCADE SAS
 //
 // This file is part of Open CASCADE Technology software library.
 //
 // This library is free software; you can redistribute it and/or modify it under
 // the terms of the GNU Lesser General Public License version 2.1 as published
 // by the Free Software Foundation, with special exception defined in the file
 // OCCT_LGPL_EXCEPTION.txt. Consult the file LICENSE_LGPL_21.txt included in OCCT
 // distribution for complete text of the license and disclaimer of any warranty.
 //
 // Alternatively, this file may be used under the terms of Open CASCADE
 // commercial license or contractual agreement.
 
 #ifndef _math_PSO_HeaderFile
 #define _math_PSO_HeaderFile
 
 #include <math_MultipleVarFunction.hxx>
 #include <math_Vector.hxx>
 
 class math_PSOParticlesPool;
 
 //! In this class implemented variation of Particle Swarm Optimization (PSO) method.
 //! A. Ismael F. Vaz, L. N. Vicente 
 //! "A particle swarm pattern search method for bound constrained global optimization"
 //!
 //! Algorithm description:
 //! Init Section:
 //! At start of computation a number of "particles" are placed in the search space.
 //! Each particle is assigned a random velocity.
 //!
 //! Computational loop:
 //! The particles are moved in cycle, simulating some "social" behavior, so that new position of
 //! a particle on each step depends not only on its velocity and previous path, but also on the
 //! position of the best particle in the pool and best obtained position for current particle.
 //! The velocity of the particles is decreased on each step, so that convergence is guaranteed.
 //!
 //! Algorithm output:
 //! Best point in param space (position of the best particle) and value of objective function.
 //!
 //! Pros:
 //! One of the fastest algorithms.
 //! Work over functions with a lot local extremums.
 //! Does not require calculation of derivatives of the functional.
 //!
 //! Cons:
 //! Convergence to global minimum not proved, which is a typical drawback for all stochastic algorithms.
 //! The result depends on random number generator.
 //!
 //! Warning: PSO is effective to walk into optimum surrounding, not to get strict optimum.
 //! Run local optimization from pso output point.
 //! Warning: In PSO used fixed seed in RNG, so results are reproducible.
 
 class math_PSO
 {
 public:
 
   /**
   * Constructor.
   *
   * @param theFunc defines the objective function. It should exist during all lifetime of class instance.
   * @param theLowBorder defines lower border of search space.
   * @param theUppBorder defines upper border of search space.
   * @param theSteps defines steps of regular grid, used for particle generation.
                     This parameter used to define stop condition (TerminalVelocity).
   * @param theNbParticles defines number of particles.
   * @param theNbIter defines maximum number of iterations.
   */
   Standard_EXPORT math_PSO(math_MultipleVarFunction* theFunc,
                            const math_Vector& theLowBorder,
                            const math_Vector& theUppBorder,
                            const math_Vector& theSteps,
                            const Standard_Integer theNbParticles = 32,
                            const Standard_Integer theNbIter = 100);
 
   //! Perform computations, particles array is constructed inside of this function.
   Standard_EXPORT void Perform(const math_Vector& theSteps,
                                Standard_Real& theValue,
                                math_Vector& theOutPnt,
                                const Standard_Integer theNbIter = 100);
 
   //! Perform computations with given particles array.
   Standard_EXPORT void Perform(math_PSOParticlesPool& theParticles,
                                Standard_Integer theNbParticles,
                                Standard_Real& theValue,
                                math_Vector& theOutPnt,
                                const Standard_Integer theNbIter = 100);
 
 private:
 
   void performPSOWithGivenParticles(math_PSOParticlesPool& theParticles,
                                     Standard_Integer theNbParticles,
                                     Standard_Real& theValue,
                                     math_Vector& theOutPnt,
                                     const Standard_Integer theNbIter = 100);
 
   math_MultipleVarFunction *myFunc;
   math_Vector myLowBorder; // Lower border.
   math_Vector myUppBorder; // Upper border.
   math_Vector mySteps; // steps used in PSO algorithm.
   Standard_Integer myN; // Dimension count.
   Standard_Integer myNbParticles; // Particles number.
   Standard_Integer myNbIter;
 };
 
 #endif

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