ALPSO
Augmented Lagrangian Particle Swarm Optimizer (ALPSO) is a PSO method that uses the augmented Lagrangian approach to handle constraints.
Options
Name 
Type 
Default value 
Description 


int 
40 
Number of Particles (Depends on Problem dimensions) 

int 
200 
Maximum Number of Outer Loop Iterations (Major Iterations) 

int 
6 
Maximum Number of Inner Loop Iterations (Minor Iterations) 

int 
6 
Minimum Number of Inner Loop Iterations (Dynamic Inner Iterations) 

int 
0 
Dynamic Number of Inner Iterations Flag 

int 
1 
Stopping Criteria Flag (0  maxIters, 1  convergence) 

int 
5 
Consecutive Number of Iterations for which the Stopping Criteria must be Satisfied 

float 
0.001 
Absolute Tolerance for Equality constraints 

float 
0.001 
Absolute Tolerance for Inequality constraints 

float 
0.01 
Relative Tolerance for Lagrange Multipliers 

float 
0.01 
Absolute Tolerance for Lagrange Function 

float 
0.1 
Relative Tolerance in Distance of All Particles to Terminate (GCPSO) 

int 
0 
Number of Iterations Before Print Outer Loop Information 

int 
0 
Number of Iterations Before Print Inner Loop Information 

float 
1.0 
Initial Penalty Factor 

int 
0 
Initial Position Flag (0  no position, 1  position given) 

float 
1.0 
Initial Velocity of Particles in Normalized [1, 1] Design Space 

float 
2.0 
Maximum Velocity of Particles in Normalized [1, 1] Design Space 

float 
2.0 
Cognitive Parameter 

float 
1.0 
Social Parameter 

float 
0.99 
Initial Inertia Weight 

float 
0.55 
Final Inertia Weight 

int 
15 
Number of Consecutive Successes in Finding New Best Position of Best Particle Before Search Radius will be Increased (GCPSO) 

int 
5 
Number of Consecutive Failures in Finding New Best Position of Best Particle Before Search Radius will be Increased (GCPSO) 

float 
1.0 
Time step 

float 
0.0001 
Craziness Velocity (Added to Particle Velocity After Updating the Penalty Factors and Langangian Multipliers) 

int 
1 
Flag to Turn On Output to filename 

str 

We could probably remove fileout flag if filename or fileinstance is given 

int 
0 
Random Number Seed (0  AutoSeed based on time clock) 

int 
40 
Number of Neighbours of Each Particle 

str 

Neighbourhood Model (dl/slring  Double/Single Link Ring, wheel  Wheel, Spatial  based on spatial distance, sfrac  Spatial Fraction) 

int 
1 
Selfless Neighbourhood Model (0  Include Particle i in NH i, 1  Don’t Include Particle i) 

int 
1 
Design Variables Scaling Flag (0  no scaling, 1  scaling between [1, 1]) 

str 

Type of parallelization

API
 class pyoptsparse.pyALPSO.pyALPSO.ALPSO(*args, **kwargs)[source]
ALPSO Optimizer Class  Inherited from Optimizer Abstract Class
Keyword arguments:*
pll_type > STR: ALPSO Parallel Implementation (None, SPM Static, DPM Dynamic, POAParallel 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, hotStart=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
 hotStartstr
File name of the history file to “replay” for the optimization. The optimization problem used to generate the history file specified in ‘hotStart’ must be IDENTICAL to the currently supplied ‘optProb’. By identical we mean, EVERY SINGLE PARAMETER MUST BE IDENTICAL. As soon as he requested evaluation point from ALPSO does not match the history and function evaluations revert back to normal evaluations.
Notes
The kwargs are there such that the sens= argument can be supplied (but ignored here in alpso)