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Phyton implementation of the SAPSO algorithm

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Python SAPSO and PSAPSO

Python implemetations (sequential and parallel) of the semi autonomous particle swarm optmizer (sapso)

Prerequisites

numpy

Optmizing a function:

1- Define your parameters of optmization in a dictionary as specified in the example

2- Call the optmizer of your choice passing those parameters:

   from psapso.optmizer import sapso
   sapso(parameters)

Description of files:

  • /sapso:

    SAPSO basic implementation, allows to chose between sequential and parallel gradient calculation

  • /psapso:

    Parallel SAPSO beta implementation

  • /psapso slow_info_exchange:

    Parallel SAPSO based on slow informaitone exchange beta implementation

  • /scripts:

    Scripts used to benchmark the optmizers

  • test_functions.py:

    A module composed of mathematical functions for optmization tests

  • example_parameters.py:

    An example on how to build a parameters dictionary that must be passed to the optmizer

  • /scripts/example_file_name.py.lprof:

    Binary file contaning a line_profiler (detailed information on execution time per line) of the optmizer main function (for execution time improvement purposes)

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Phyton implementation of the SAPSO algorithm

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