2 Copyright (C) 2008-2014 EDF R&D
4 This file is part of SALOME ADAO module.
6 This library is free software; you can redistribute it and/or
7 modify it under the terms of the GNU Lesser General Public
8 License as published by the Free Software Foundation; either
9 version 2.1 of the License, or (at your option) any later version.
11 This library is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 Lesser General Public License for more details.
16 You should have received a copy of the GNU Lesser General Public
17 License along with this library; if not, write to the Free Software
18 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
20 See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
22 Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 .. index:: single: ParticleSwarmOptimization
25 .. _section_ref_algorithm_ParticleSwarmOptimization:
27 Calculation algorithm "*ParticleSwarmOptimization*"
28 ---------------------------------------------------
33 This algorithm realizes an estimation of the state of a dynamic system by a
36 This is an optimization method allowing for global minimum search of a general
37 function of type :math:`L^1`, :math:`L^2` or :math:`L^{\infty}`, with or without
40 Optional and required commands
41 ++++++++++++++++++++++++++++++
43 .. index:: single: Background
44 .. index:: single: BackgroundError
45 .. index:: single: Observation
46 .. index:: single: ObservationError
47 .. index:: single: ObservationOperator
48 .. index:: single: MaximumNumberOfSteps
49 .. index:: single: NumberOfInsects
50 .. index:: single: SwarmVelocity
51 .. index:: single: GroupRecallRate
52 .. index:: single: QualityCriterion
53 .. index:: single: BoxBounds
54 .. index:: single: SetSeed
55 .. index:: single: StoreInternalVariables
56 .. index:: single: StoreSupplementaryCalculations
58 The general required commands, available in the editing user interface, are the
62 *Required command*. This indicates the background or initial vector used,
63 previously noted as :math:`\mathbf{x}^b`. Its value is defined as a
64 "*Vector*" or a *VectorSerie*" type object.
67 *Required command*. This indicates the background error covariance matrix,
68 previously noted as :math:`\mathbf{B}`. Its value is defined as a "*Matrix*"
69 type object, a "*ScalarSparseMatrix*" type object, or a
70 "*DiagonalSparseMatrix*" type object.
73 *Required command*. This indicates the observation vector used for data
74 assimilation or optimization, previously noted as :math:`\mathbf{y}^o`. It
75 is defined as a "*Vector*" or a *VectorSerie* type object.
78 *Required command*. This indicates the observation error covariance matrix,
79 previously noted as :math:`\mathbf{R}`. It is defined as a "*Matrix*" type
80 object, a "*ScalarSparseMatrix*" type object, or a "*DiagonalSparseMatrix*"
84 *Required command*. This indicates the observation operator, previously
85 noted :math:`H`, which transforms the input parameters :math:`\mathbf{x}` to
86 results :math:`\mathbf{y}` to be compared to observations
87 :math:`\mathbf{y}^o`. Its value is defined as a "*Function*" type object or
88 a "*Matrix*" type one. In the case of "*Function*" type, different
89 functional forms can be used, as described in the section
90 :ref:`section_ref_operator_requirements`. If there is some control :math:`U`
91 included in the observation, the operator has to be applied to a pair
94 The general optional commands, available in the editing user interface, are
95 indicated in :ref:`section_ref_assimilation_keywords`. In particular, the
96 optional command "*AlgorithmParameters*" allows to choose the specific options,
97 described hereafter, of the algorithm. See
98 :ref:`section_ref_options_AlgorithmParameters` for the good use of this command.
100 The options of the algorithm are the following:
103 This key indicates the maximum number of iterations allowed for iterative
104 optimization. The default is 50, which is an arbitrary limit. It is then
105 recommended to adapt this parameter to the needs on real problems.
107 Example : ``{"MaximumNumberOfSteps":100}``
110 This key indicates the number of insects or particles in the swarm. The
111 default is 100, which is a usual default for this algorithm.
113 Example : ``{"NumberOfInsects":100}``
116 This key indicates the part of the insect velocity which is imposed by the
117 swarm. It is a positive floating point value. The default value is 1.
119 Example : ``{"SwarmVelocity":1.}``
122 This key indicates the recall rate at the best swarm insect. It is a
123 floating point value between 0 and 1. The default value is 0.5.
125 Example : ``{"GroupRecallRate":0.5}``
128 This key indicates the quality criterion, minimized to find the optimal
129 state estimate. The default is the usual data assimilation criterion named
130 "DA", the augmented weighted least squares. The possible criteria has to be
131 in the following list, where the equivalent names are indicated by the sign
132 "=": ["AugmentedWeightedLeastSquares"="AWLS"="DA",
133 "WeightedLeastSquares"="WLS", "LeastSquares"="LS"="L2",
134 "AbsoluteValue"="L1", "MaximumError"="ME"].
136 Example : ``{"QualityCriterion":"DA"}``
139 This key allows to define upper and lower bounds for *increments* on every
140 state variable being optimized (and not on state variables themselves).
141 Bounds have to be given by a list of list of pairs of lower/upper bounds for
142 each increment on variable, with extreme values every time there is no bound
143 (``None`` is not allowed when there is no bound). This key is required and
144 there is no default values.
146 Example : ``{"BoxBounds":[[-0.5,0.5],[0.01,2.],[0.,1.e99],[-1.e99,1.e99]]}``
149 This key allow to give an integer in order to fix the seed of the random
150 generator used to generate the ensemble. A convenient value is for example
151 1000. By default, the seed is left uninitialized, and so use the default
152 initialization from the computer.
154 Example : ``{"SetSeed":1000}``
156 StoreInternalVariables
157 This Boolean key allows to store default internal variables, mainly the
158 current state during iterative optimization process. Be careful, this can be
159 a numerically costly choice in certain calculation cases. The default is
162 Example : ``{"StoreInternalVariables":True}``
164 StoreSupplementaryCalculations
165 This list indicates the names of the supplementary variables that can be
166 available at the end of the algorithm. It involves potentially costly
167 calculations or memory consumptions. The default is a void list, none of
168 these variables being calculated and stored by default. The possible names
169 are in the following list: ["BMA", "OMA", "OMB", "Innovation"].
171 Example : ``{"StoreSupplementaryCalculations":["BMA","Innovation"]}``
176 References to other sections: