ALPSO

Augmented Lagrangian Particle Swarm Optimizer (ALPSO) is a PSO method that uses the augmented Lagrangian approach to handle constraints.

Options

Option name

Type

Default value

SwarmSize

int

40

maxOuterIter

int

200

maxInnerIter

int

6

minInnerIter

int

6

dynInnerIter

int

0

stopCriteria

int

1

stopIters

int

5

etol

float

0.001

itol

float

0.001

rtol

float

0.01

atol

float

0.01

dtol

float

0.1

printOuterIters

int

0

printInnerIters

int

0

rinit

float

1.0

xinit

int

0

vinit

float

1.0

vmax

float

2.0

c1

float

2.0

c2

float

1.0

w1

float

0.99

w2

float

0.55

ns

int

15

nf

int

5

dt

float

1.0

vcrazy

float

0.0001

fileout

int

1

filename

str

ALPSO.out

seed

int

0

HoodSize

int

40

HoodModel

str

gbest

HoodSelf

int

1

Scaling

int

1

parallelType

str

[‘’, ‘EXT’]

Informs

Code

Description

API

class pyoptsparse.pyALPSO.pyALPSO.ALPSO(*args: Any, **kwargs: Any)[source]

ALPSO Optimizer Class - Inherited from Optimizer Abstract Class

Keyword arguments:*

  • pll_type -> STR: ALPSO Parallel Implementation (None, SPM- Static, DPM- Dynamic, POA-Parallel Analysis), Default = None

This is the base optimizer class that all optimizers inherit from. We define common methods here to avoid code duplication.

Parameters
namestr

Optimizer name

categorystr

Typically local or global

defaultOptionsdictionary

A dictionary containing the default options

informsdict

Dictionary of the inform codes

__call__(optProb, storeHistory=None, **kwargs)[source]

This is the main routine used to solve the optimization problem.

Parameters
optProbOptimization or Solution class instance

This is the complete description of the optimization problem to be solved by the optimizer

storeHistorystr

File name of the history file into which the history of this optimization will be stored

Notes

The kwargs are there such that the sens= argument can be supplied (but ignored here in alpso)