13.13. Calculation algorithm “ParticleSwarmOptimization”¶

13.13.1. Description¶

This algorithm realizes an estimation of the state of a system by minimization without gradient of a cost function $J$ by using an evolutionary strategy of particle swarm. It is a method that does not use the derivatives of the cost function. It falls in the same category than the Calculation algorithm “DerivativeFreeOptimization”, the Calculation algorithm “DifferentialEvolution” or the Calculation algorithm “TabuSearch”.

This is a mono-objective optimization method, allowing for global minimum search of a general error function $J$ of type $L^1$ , $L^2$ or $L^{\infty}$ , with or without weights, as described in the section for Going further in the state estimation by optimization methods. The default error function is the augmented weighted least squares function, classically used in data assimilation.

It is based on the evolution of a population (called a “swarm”) of states (each state is called a “particle” or an “insect”). There exists various variants of this algorithm. The following stable and robust formulations are proposed here:

“CanonicalPSO” (Canonical Particle Swarm Optimization, see [ZambranoBigiarini13]), classical algorithm called “canonical” of particle swarm, robust and defining a reference for particle swarm algorithms,
“OGCR” (Simple Particle Swarm Optimization), simplified algorithm of particle swarm with no bounds on insects or velocities, not recommended because less robust, but sometimes a lot more efficient,
“SPSO-2011” (Standard Particle Swarm Optimization 2011, voir [ZambranoBigiarini13]), 2011 reference algorithm of particle swarm, robust, efficient and defined as a reference for particle swarm algorithms. This algorithm is sometimes called “ $\omega$ -PSO” or “Inertia PSO” because it incorporates a so-called inertia contribution, or also called “AIS” (for “Asynchronous Iteration Strategy”) or “APSO” (for “Advanced Particle Swarm Optimization”) because it incorporates evolutionary updating of the best elements, leading to intrinsically improved convergence of the algorithm.
“SPSO-2011-SIS” (Standard Particle Swarm Optimisation 2011 with Synchronous Iteration Strategy), very similar to the 2011 reference algorithm, and with a synchronous particle update, called “SIS”,
“SPSO-2011-PSIS” (Standard Particle Swarm Optimisation 2011 with Parallel Synchronous Iteration Strategy), similar to the “SPSO-2011-SIS” algorithm with synchronous updating and parallelization, known as “PSIS”, of the particles.

The following are a few practical suggestions for the effective use of these algorithms:

The recommended variant of this algorithm is the “SPSO-2011” even if the “CanonicalPSO” algorithm remains by default the more robust one. If the state evaluation can be carried out in parallel, the “SPSO-2011-PSIS” algorithm can be used, even if its convergence is sometimes a little less efficient.
The number of particles or insects usually recommended varies between 40 and 100 depending on the algorithm, more or less independently of the dimension of the state space. Usually, the best performances are obtained for populations of 70 to 500 particles. Even if the default value for this elementary parameter comes from extended knowledge on these algorithms, it is recommended to adapt it to the difficulty of the given problems.
The recommended number of generations for population evolution is often around 50, but it can easily vary between 25 and 500.
The maximum number of evaluations of the simulation function should usually be limited to between a few thousand and a few tens of thousands of times the dimension of the state space.
The error functional usually decreases by levels (thus with a zero progression of the value of the functional at each generation when we stay in the level), making it not recommended to stop on the criterion of decrease of the cost function. It is normally wiser to adapt the number of iterations or generations to accelerate the convergence of the algorithms.
If the problem is constrained, it is necessary to define the bounds of the variables (by the variable “Bounds”). If the problem is totally unconstrained, it is essential to define increment bounds (by the variable “BoxBounds”) to delimit the optimal search in a useful way. Similarly, if the problem is partially constrained, it is recommended (but not required) to define increment bounds. In case these increment bounds are not defined, the variable bounds will be used as increment bounds.

These suggestions are to be used as experimental indications, not as requirements, because they are to be appreciated or adapted according to the physics of each problem that is treated.

The count of the number of evaluations of the function to be simulated during this algorithm is deterministic, namely the “number of iterations or generations” multiplied by the “number of individuals in the population”. With the default values, it takes between 40x50=2000 and 100*50=5000 evaluations. It is for this reason that this algorithm is usually interesting when the dimension of the state space is large, or when the non-linearities of the simulation make the evaluation of the gradient of the functional by numerical approximation complicated or invalid. But it is also necessary that the calculation of the function to be simulated is not too costly to avoid a prohibitive optimization time length.

13.13.2. Some noteworthy properties of the implemented methods¶

To complete the description, we summarize here a few notable properties of the algorithm methods or of their implementations. These properties may have an influence on how it is used or on its computational performance. For further information, please refer to the more comprehensive references given at the end of this algorithm description.

The optimization methods proposed by this algorithm perform a non-local search for the minimum, without however ensuring a global search. This is the case when optimization methods have the ability to avoid being trapped by the first local minimum found. These capabilities are sometimes heuristic.

The methods proposed by this algorithm do not require derivation of the objective function or of one of the operators, thus avoiding this additional cost when derivatives are calculated numerically by multiple evaluations.

The methods proposed by this algorithm have internal parallelism, and can therefore take advantage of computational distribution resources. The potential interaction, between the parallelism of the numerical derivation, and the parallelism that may be present in the observation or evolution operators embedding user codes, must therefore be carefully tuned.

The methods proposed by this algorithm achieve their convergence on one or more number criteria. In practice, there may be simultaneously several convergence criteria.

The number is frequently a significant value for the algorithm, such as a number of iterations or a number of evaluations, but it can also be, for example, a number of generations for an evolutionary algorithm.

Convergence thresholds need to be carefully adjusted, to reduce the gobal calculation cost, or to ensure that convergence is adapted to the physical case encountered.

13.13.3. Optional and required commands¶

The general required commands, available in the editing user graphical or textual interface, are the following:

Background: Vector. The variable indicates the background or initial vector used, previously noted as $\mathbf{x}^b$ . Its value is defined as a “Vector” or “VectorSerie” type object. Its availability in output is conditioned by the boolean “Stored” associated with input.

BackgroundError: Matrix. This indicates the background error covariance matrix, previously noted as $\mathbf{B}$ . Its value is defined as a “Matrix” type object, a “ScalarSparseMatrix” type object, or a “DiagonalSparseMatrix” type object, as described in detail in the section Requirements to describe covariance matrices. Its availability in output is conditioned by the boolean “Stored” associated with input.

Observation: List of vectors. The variable indicates the observation vector used for data assimilation or optimization, and usually noted $\mathbf{y}^o$ . Its value is defined as an object of type “Vector” if it is a single observation (temporal or not) or “VectorSeries” if it is a succession of observations. Its availability in output is conditioned by the boolean “Stored” associated in input.

ObservationError: Matrix. The variable indicates the observation error covariance matrix, usually noted as $\mathbf{R}$ . It is defined as a “Matrix” type object, a “ScalarSparseMatrix” type object, or a “DiagonalSparseMatrix” type object, as described in detail in the section Requirements to describe covariance matrices. Its availability in output is conditioned by the boolean “Stored” associated with input.

ObservationOperator: Operator. The variable indicates the observation operator, usually noted as $H$ , which transforms the input parameters $\mathbf{x}$ to results $\mathbf{y}$ to be compared to observations $\mathbf{y}^o$ . Its value is defined as a “Function” type object or a “Matrix” type one. In the case of “Function” type, different functional forms can be used, as described in the section Requirements for functions describing an operator. If there is some control $U$ included in the observation, the operator has to be applied to a pair $(X,U)$ .

The general optional commands, available in the editing user graphical or textual interface, are indicated in List of commands and keywords for data assimilation or optimization case. Moreover, the parameters of the command “AlgorithmParameters” allows to choose the specific options, described hereafter, of the algorithm. See Description of options of an algorithm by “AlgorithmParameters” for the good use of this command.

The options are the following:

Bounds

List of pairs of real values. This key allows to define pairs of upper and lower bounds for every state variable being optimized. Bounds have to be given by a list of list of pairs of lower/upper bounds for each variable, with a value of None each time there is no bound. The bounds can always be specified, but they are taken into account only by the constrained optimizers. If the list is empty, there are no bounds.

Example: {"Bounds":[[2.,5.],[1.e-2,10.],[-30.,None],[None,None]]}

BoxBounds

List of pairs of real values. This key allows to define pairs of upper and lower bounds for increments on every state variable being optimized (and not on state variables themselves, whose bounds can be indicated by the “Bounds” variable). Increment bounds have to be given by a list of list of pairs of lower/upper bounds for each increment on variable, with a value of None each time there is no bound. This key is only required if there are no variable bounds, and there are no default values.

Example : {"BoxBounds":[[-0.5,0.5], [0.01,2.], [0.,None], [None,None]]}

CognitiveAcceleration

Real value. This key indicates the recall rate at the best previously known value of the current insect. It is a floating point positive value. The default value is about $1/2+ln(2)$ , and it is recommended to adapt it, rather by reducing it, to the physical case that is being treated.

Example : {"CognitiveAcceleration":1.19315}

InertiaWeight

Real value. This key indicates the part of the insect velocity which is imposed by the swarm, named “inertia weight”. It is a positive floating point value. It is a floating point value between 0 and 1. The default value is about $1/(2*ln(2))$ and it is recommended to adapt it to the physical case that is being treated.

Example : {"InertiaWeight":0.72135}

InitializationPoint

Vector. The variable specifies one vector to be used as the initial state around which an iterative algorithm starts. By default, this initial state is not required and is equal to the background $\mathbf{x}^b$ . Its value must allow to build a vector of the same size as the background. If provided, it replaces the background only for initialization.

Example : {"InitializationPoint":[1, 2, 3, 4, 5]}

MaximumNumberOfFunctionEvaluations

Integer value. This key indicates the maximum number of evaluation of the cost function to be optimized. The default is 15000, which is an arbitrary limit. It is then recommended to adapt this parameter to the needs on real problems. For some optimizers, the effective number of function evaluations can be slightly different of the limit due to algorithm internal control requirements.

Example: {"MaximumNumberOfFunctionEvaluations":50}

MaximumNumberOfIterations

Integer value. This key indicates the maximum number of internal iterations allowed for iterative optimization. The default is 50, which is an arbitrary limit. It is then recommended to adapt this parameter to the needs on real problems.

Example: {"MaximumNumberOfIterations":50}

NumberOfInsects

Integer value. This key indicates the number of insects or particles in the swarm. The default is 100, which is a usual default for this algorithm.

Example : {"NumberOfInsects":100}

QualityCriterion

Predefined name. This key indicates the quality criterion, minimized to find the optimal state estimate. The default is the usual data assimilation criterion named “DA”, the augmented weighted least squares. The possible criterion has to be in the following list, where the equivalent names are indicated by the sign “<=>”: [“AugmentedWeightedLeastSquares” <=> “AWLS” <=> “DA”, “WeightedLeastSquares” <=> “WLS”, “LeastSquares” <=> “LS” <=> “L2”, “AbsoluteValue” <=> “L1”, “MaximumError” <=> “ME” <=> “Linf”]. See the section for Going further in the state estimation by optimization methods to have a detailed definition of these quality criteria.

Example: {"QualityCriterion":"DA"}

SetSeed

Integer value. This key allow to give an integer in order to fix the seed of the random generator used in the algorithm. By default, the seed is left uninitialized, and so use the default initialization from the computer, which then change at each study. To ensure the reproducibility of results involving random samples, it is strongly advised to initialize the seed. A simple convenient value is for example 123456789. It is recommended to put an integer with more than 6 or 7 digits to properly initialize the random generator.

Example: {"SetSeed":123456789}

SocialAcceleration

Real value. This key indicates the recall rate at the best swarm insect in the neighbourhood of the current insect, which is by default the whole swarm. It is a floating point positive value. The default value is about $1/2+ln(2)=1.19315$ and it is recommended to adapt it, rather by reducing it, to the physical case that is being treated.

Example : {"SocialAcceleration":1.19315}

StoreSupplementaryCalculations

List of names. This list indicates the names of the supplementary variables, that can be available during or at the end of the algorithm, if they are initially required by the user. Their availability involves, potentially, costly calculations or memory consumptions. The default is then a void list, none of these variables being calculated and stored by default (excepted the unconditional variables). The possible names are in the following list (the detailed description of each named variable is given in the following part of this specific algorithmic documentation, in the sub-section “Information and variables available at the end of the algorithm”): [ “Analysis”, “BMA”, “CostFunctionJ”, “CostFunctionJb”, “CostFunctionJo”, “CurrentIterationNumber”, “CurrentState”, “Innovation”, “InternalCostFunctionJ”, “InternalCostFunctionJb”, “InternalCostFunctionJo”, “InternalStates”, “OMA”, “OMB”, “SimulatedObservationAtBackground”, “SimulatedObservationAtCurrentState”, “SimulatedObservationAtOptimum”, ].

Example : {"StoreSupplementaryCalculations":["CurrentState", "Residu"]}

SwarmTopology

Predefined name. This key indicates how the particles (or insects) communicate information to each other during the evolution of the particle swarm. The most classical method consists in exchanging information between all particles (called “gbest” or “FullyConnectedNeighborhood”). But it is often more efficient to exchange information on a reduced neighborhood, as in the classical method “lbest” (or “RingNeighborhoodWithRadius1”) exchanging information with the two neighboring particles in numbering order (the previous one and the next one), or the method “RingNeighborhoodWithRadius2” exchanging with the 4 neighbors (the two previous ones and the two following ones). A variant of reduced neighborhood consists in exchanging with 3 neighbors (method “AdaptativeRandomWith3Neighbors”) or 5 neighbors (method “AdaptativeRandomWith5Neighbors”) chosen randomly (the particle can be drawn several times). The default value is “FullyConnectedNeighborhood”, and it is advisable to change it carefully depending on the properties of the simulated physical system. The possible communication topology is to be chosen from the following list, in which the equivalent names are indicated by a “<=>” sign: [“FullyConnectedNeighborhood” <=> “FullyConnectedNeighbourhood” <=> “gbest”, “RingNeighborhoodWithRadius1” <=> “RingNeighbourhoodWithRadius1” <=> “lbest”, “RingNeighborhoodWithRadius2” <=> “RingNeighbourhoodWithRadius2”, “AdaptativeRandomWith3Neighbors” <=> “AdaptativeRandomWith3Neighbours” <=> “abest”, “AdaptativeRandomWith5Neighbors” <=> “AdaptativeRandomWith5Neighbours”].

Example : {"SwarmTopology":"FullyConnectedNeighborhood"}

Variant

Predefined name. This key allows to choose one of the possible variants for the main algorithm. The default variant is the original “CanonicalPSO”, and the possible choices are “CanonicalPSO” (Canonical Particle Swarm Optimization), “OGCR” (Simple Particle Swarm Optimization), “SPSO-2011” (Standard Standard Particle Swarm Optimization 2011).

It is recommended to try the “CanonicalPSO” variant with about 100 particles for robust performance, and to reduce the number of particles to about 40 for all variants other than the original “CanonicalPSO” formulation.

Example : {"Variant":"CanonicalPSO"}

VelocityClampingFactor

Real value. This key indicates the rate of group velocity attenuation in the update for each insect, useful to avoid swarm explosion, i.e. uncontrolled growth of insect velocity. It is a floating point value between 0+ and 1. The default value is 0.3.

Example : {"VelocityClampingFactor":0.3}

13.13.4. Information and variables available at the end of the algorithm¶

At the output, after executing the algorithm, there are information and variables originating from the calculation. The description of Variables and information available at the output show the way to obtain them by the method named get, of the variable “ADD” of the post-processing in graphical interface, or of the case in textual interface. The input variables, available to the user at the output in order to facilitate the writing of post-processing procedures, are described in the Inventory of potentially available information at the output.

Permanent outputs (non conditional)

The unconditional outputs of the algorithm are the following:

Analysis

List of vectors. Each element of this variable is an optimal state $\mathbf{x}^*$ in optimization, an interpolate or an analysis $\mathbf{x}^a$ in data assimilation.

Example: xa = ADD.get("Analysis")[-1]

CostFunctionJ

List of values. Each element is a value of the chosen error function $J$ .

Example: J = ADD.get("CostFunctionJ")[:]

CostFunctionJb

List of values. Each element is a value of the error function $J^b$ , that is of the background difference part. If this part does not exist in the error function, its value is zero.

Example: Jb = ADD.get("CostFunctionJb")[:]

CostFunctionJo

List of values. Each element is a value of the error function $J^o$ , that is of the observation difference part.

Example: Jo = ADD.get("CostFunctionJo")[:]

Set of on-demand outputs (conditional or not)

The whole set of algorithm outputs (conditional or not), sorted by alphabetical order, is the following:

Analysis

List of vectors. Each element of this variable is an optimal state $\mathbf{x}^*$ in optimization, an interpolate or an analysis $\mathbf{x}^a$ in data assimilation.

Example: xa = ADD.get("Analysis")[-1]

BMA

List of vectors. Each element is a vector of difference between the background and the optimal state.

Example: bma = ADD.get("BMA")[-1]

CostFunctionJ

List of values. Each element is a value of the chosen error function $J$ .

Example: J = ADD.get("CostFunctionJ")[:]

CostFunctionJb

List of values. Each element is a value of the error function $J^b$ , that is of the background difference part. If this part does not exist in the error function, its value is zero.

Example: Jb = ADD.get("CostFunctionJb")[:]

CostFunctionJo

List of values. Each element is a value of the error function $J^o$ , that is of the observation difference part.

Example: Jo = ADD.get("CostFunctionJo")[:]

CurrentIterationNumber

List of integers. Each element is the iteration index at the current step during the iterative algorithm procedure. There is one iteration index value per assimilation step corresponding to an observed state.

Example: cin = ADD.get("CurrentIterationNumber")[-1]

CurrentState

List of vectors. Each element is a usual state vector used during the iterative algorithm procedure.

Example: xs = ADD.get("CurrentState")[:]

Innovation

List of vectors. Each element is an innovation vector, which is in static the difference between the optimal and the background, and in dynamic the evolution increment.

Example: d = ADD.get("Innovation")[-1]

OMA

List of vectors. Each element is a vector of difference between the observation and the optimal state in the observation space.

Example: oma = ADD.get("OMA")[-1]

OMB

List of vectors. Each element is a vector of difference between the observation and the background state in the observation space.

Example: omb = ADD.get("OMB")[-1]

SimulatedObservationAtBackground

List of vectors. Each element is a vector of observation simulated by the observation operator from the background $\mathbf{x}^b$ . It is the forecast from the background, and it is sometimes called “Dry”.

Example: hxb = ADD.get("SimulatedObservationAtBackground")[-1]

SimulatedObservationAtCurrentState

List of vectors. Each element is an observed vector simulated by the observation operator from the current state, that is, in the observation space.

Example: hxs = ADD.get("SimulatedObservationAtCurrentState")[-1]

SimulatedObservationAtOptimum

List of vectors. Each element is a vector of observation obtained by the observation operator from simulation on the analysis or optimal state $\mathbf{x}^a$ . It is the observed forecast from the analysis or the optimal state, and it is sometimes called “Forecast”.

Example: hxa = ADD.get("SimulatedObservationAtOptimum")[-1]

13.13.5. Python (TUI) use examples¶

Here is one or more very simple examples of the proposed algorithm and its parameters, written in [DocR] Textual User Interface for ADAO (TUI/API). Moreover, when it is possible, the information given as input also allows to define an equivalent case in [DocR] Graphical User Interface for ADAO (GUI/EFICAS).

This example describes the calibration of parameters $\mathbf{x}$ of a quadratic observation model $H$ . This model is here represented as a function named QuadFunction. This function get as input the coefficients vector $\mathbf{x}$ , and return as output the evaluation vector $\mathbf{y}$ of the quadratic model at the predefined internal control points. The calibration is done using an initial coefficient set (background state specified by Xb in the code), and with the information $\mathbf{y}^o$ (specified by Yobs in the code) of 5 measures obtained in these same internal control points. We set twin experiments (see To test a data assimilation chain: the twin experiments) and the measurements are supposed to be perfect. We choose to emphasize the observations versus the background by setting a great variance for the background error, here of $10^{6}$ .

The adjustment is carried out by displaying intermediate results during iterative optimization.

# -*- coding: utf-8 -*-
#
from numpy import array, ravel
def QuadFunction( coefficients ):
    """
    Quadratic simulation in x: y = a x^2 + b x + c
    """
    a, b, c = list(ravel(coefficients))
    x_points = (-5, 0, 1, 3, 10)
    y_points = []
    for x in x_points:
        y_points.append( a*x*x + b*x + c )
    return array(y_points)
#
Xb   = array([1., 1., 1.])
Yobs = array([57, 2, 3, 17, 192])
#
NumberOfInsects = 40
#
print("Resolution of the calibration problem")
print("-------------------------------------")
print("")
from adao import adaoBuilder
case = adaoBuilder.New()
case.setBackground( Vector = Xb, Stored=True )
case.setBackgroundError( ScalarSparseMatrix = 1.e6 )
case.setObservation( Vector = Yobs, Stored=True )
case.setObservationError( ScalarSparseMatrix = 1. )
case.setObservationOperator( OneFunction = QuadFunction )
case.setAlgorithmParameters(
    Algorithm='ParticleSwarmOptimization',
    Parameters={
        'NumberOfInsects':NumberOfInsects,
        'MaximumNumberOfIterations': 20,
        'StoreSupplementaryCalculations': [
            'CurrentState',
            ],
        'Bounds':[[0,5],[-2,2],[0,5]],
        'SetSeed':123456789,
        },
    )
case.setObserver(
    Info="  Intermediate state at the current iteration:",
    Template='ValuePrinter',
    Variable='CurrentState',
    )
case.execute()
print("")
#
#-------------------------------------------------------------------------------
#
print("Calibration of %i coefficients in a 1D quadratic function on %i measures"%(
    len(case.get('Background')),
    len(case.get('Observation')),
    ))
print("----------------------------------------------------------------------")
print("")
print("Observation vector.................:", ravel(case.get('Observation')))
print("A priori background state..........:", ravel(case.get('Background')))
print("")
print("Expected theoretical coefficients..:", ravel((2,-1,2)))
print("")
print("Number of iterations...............:", len(case.get('CurrentState')))
print("Number of simulations..............:", NumberOfInsects*len(case.get('CurrentState')))
print("Calibration resulting coefficients.:", ravel(case.get('Analysis')[-1]))
#
Xa = case.get('Analysis')[-1]
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10, 4)
#
plt.figure()
plt.plot((-5,0,1,3,10),QuadFunction(Xb),'b-',label="Simulation at background")
plt.plot((-5,0,1,3,10),Yobs,            'kX',label='Observation',markersize=10)
plt.plot((-5,0,1,3,10),QuadFunction(Xa),'r-',label="Simulation at optimum")
plt.legend()
plt.title('Coefficients calibration', fontweight='bold')
plt.xlabel('Arbitrary coordinate')
plt.ylabel('Observations')
plt.savefig("simple_ParticleSwarmOptimization1.png")

The execution result is the following:

Resolution of the calibration problem
-------------------------------------

  Intermediate state at the current iteration: [ 1.76770856 -1.2054263   1.22625259]
  Intermediate state at the current iteration: [ 2.04776518 -1.17449716  3.14493347]
  Intermediate state at the current iteration: [ 2.04776518 -1.17449716  3.14493347]
  Intermediate state at the current iteration: [ 2.01933291 -1.          3.15162067]
  Intermediate state at the current iteration: [ 1.96384202 -0.74855119  3.40642058]
  Intermediate state at the current iteration: [ 1.96384202 -0.74855119  3.40642058]
  Intermediate state at the current iteration: [ 1.96384202 -0.74855119  3.40642058]
  Intermediate state at the current iteration: [ 1.96384202 -0.74855119  3.40642058]
  Intermediate state at the current iteration: [ 1.95417745 -0.73191939  3.14451887]
  Intermediate state at the current iteration: [ 1.96217646 -0.7883895   2.91127919]
  Intermediate state at the current iteration: [ 1.97610485 -1.00254825  2.89582746]
  Intermediate state at the current iteration: [ 2.0007262  -1.06443275  2.69825603]
  Intermediate state at the current iteration: [ 1.9934285  -1.02432071  2.39823319]
  Intermediate state at the current iteration: [ 1.9934285  -1.02432071  2.39823319]
  Intermediate state at the current iteration: [ 1.9942533  -0.99256953  2.30174702]
  Intermediate state at the current iteration: [ 1.9942533  -0.99256953  2.30174702]
  Intermediate state at the current iteration: [ 1.99742923 -0.99796085  2.1278678 ]
  Intermediate state at the current iteration: [ 1.99742923 -0.99796085  2.1278678 ]
  Intermediate state at the current iteration: [ 1.99742923 -0.99796085  2.1278678 ]
  Intermediate state at the current iteration: [ 1.99742923 -0.99796085  2.1278678 ]
  Intermediate state at the current iteration: [ 2.00166149 -1.0012696   2.02137857]

Calibration of 3 coefficients in a 1D quadratic function on 5 measures
----------------------------------------------------------------------

Observation vector.................: [ 57.   2.   3.  17. 192.]
A priori background state..........: [1. 1. 1.]

Expected theoretical coefficients..: [ 2 -1  2]

Number of iterations...............: 21
Number of simulations..............: 840
Calibration resulting coefficients.: [ 2.00166149 -1.0012696   2.02137857]

The figures illustrating the result of its execution are as follows:

_images/simple_ParticleSwarmOptimization1.png

13.13.6. See also¶

References to other sections:

Bibliographical references: