Scilab / Windows : parallelization of the genetic algorithm optim_GA

Scilab includes a genetic algorithm in the function optim_GA.

To save time when evaluating each individual in a generation, it is possible to parallelize the evaluations: they are independent!

We propose here a modification of the Scilaboptim_ga routine allowing you to evaluate in parallel a number of individuals equal to the number of available threads on the computer (the reduction of computing time is thus proportional to the number of cores of your machine)

This operation is valid under Linux only, the Windows version of Scilab 6 not supporting parallelism: « In this current version of Scilab, parallel_run uses only one core on Windows platforms. » (source).

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// AREP - 16, av. d'Ivry, 7501/3 Paris, FRANCE
/////////////////////////////////////////////////////////////////////
// Scilab (www.scilab.org) - This file is part of Scilab
// Copyright (C) 2008 - Yann COLLETTE <yann[dot]collette[at]renault[dot]com>
// Copyright (C) 2014 - Michael Baudin <michael[dot]baudin[at]contrib[dot]scilab[dot]org>
//
//	26/6/2017 : added the "parallel_run" capability for the evaluation of individuals (Edouard Walther)
//
// This file must be used under the terms of the CeCILL.
// This source file is licensed as described in the file COPYING, which
// you should have received as part of this distribution.  The terms
// are also available at
// http://www.cecill.info/licences/Licence_CeCILL_V2.1-en.txt                                  //
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Last modification : 26/06/2017                                 //
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Contact : edouard[dot]walther[at]arep[dot]com                                
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////


function [pop_opt, fobj_pop_opt, pop_init, fobj_pop_init] = optim_GA_parallel(ga_f, pop_size, nb_generation, p_mut, p_cross, Log, param)

    [nargout, nargin] = argn();

    if ~isdef("param", "local") then
        param = [];
    end

    [codage_func, err]    = get_param(param, "codage_func", coding_ga_identity);
    [init_func, err]      = get_param(param, "init_func", init_ga_default);
    [crossover_func, err] = get_param(param, "crossover_func", crossover_ga_default);
    [mutation_func, err]  = get_param(param, "mutation_func", mutation_ga_default);
    [selection_func, err] = get_param(param, "selection_func", selection_ga_elitist);
    [nb_couples, err]     = get_param(param, "nb_couples", 100);
    [pressure, err]       = get_param(param, "pressure", 0.05);
    [output_func, err] = get_param(param, "output_func", output_ga_default);

    if ~isdef("ga_f", "local") then
        error(sprintf(gettext("%s: ga_f is mandatory"), "optim_ga"));
    else
        if typeof(ga_f) == "list" then
            deff("y = _ga_f(x)", "y = ga_f(1)(x, ga_f(2:$))");
        else
            deff("y = _ga_f(x)", "y = ga_f(x)");
        end
    end

    if ~isdef("pop_size", "local") then
        pop_size = 100;
    end
    if ~isdef("nb_generation", "local") then
        nb_generation = 10;
    end
    if ~isdef("p_mut", "local") then
        p_mut = 0.1;
    end
    if ~isdef("p_cross", "local") then
        p_cross = 0.7;
    end
    if ~isdef("Log", "local") then
        Log = %F;
    end

    // Initialization of the population
    Pop = list();
    Pop = init_func(pop_size, param);

    if (nargout >= 3) then
        pop_init = Pop;
    end

    // Code the individuals
    Pop = codage_func(Pop, "code", param);

	// Getting the objective function for each individual
	//disp("Extraction list...");
	[vec_param_pop]=Pop(1);
	vec_param_total=vec_param_pop;
    for i = 2:length(Pop)
        [vec_param_pop]=Pop(i);
		vec_param_total=cat(2,vec_param_total,vec_param_pop);
    end

	// First launch 
	//disp("Execution for the first population...");
	[FObj_Pop]=parallel_run(vec_param_total,_ga_f);
	FObj_Pop=FObj_Pop';
		
    if (nargout == 4) then
        fobj_pop_init = FObj_Pop;
    end

    FObj_Pop_Max = max(FObj_Pop);
    FObj_Pop_Min = min(FObj_Pop);

    // Normalization of the efficiency
    Efficiency = (1 - pressure) * (FObj_Pop_Max - FObj_Pop) / max([FObj_Pop_Max - FObj_Pop_Min %eps]) + pressure;

	
	//disp("Starting optimisation with GA...");
	
    // The genetic algorithm
    for i = 1:nb_generation
        //
        // Selection
        //
        Indiv1 = list();
        Indiv2 = list();
        Wheel = cumsum(Efficiency);
        for j = 1:nb_couples
            // Selection of the first individual in the couple
            Shoot = grand(1, 1, "unf", 0, Wheel($));
            Index = find(Shoot <= Wheel, 1);
            Indiv1(j)      = Pop(Index);
            FObj_Indiv1(j) = FObj_Pop(Index);
            // Selection of the second individual in the couple
            Shoot = grand(1, 1, "unf", 0, Wheel($));
            Index = 1;
            Index = find(Shoot <= Wheel, 1);
            Indiv2(j)      = Pop(Index);
            FObj_Indiv2(j) = FObj_Pop(Index);
        end
        //
        // Crossover
        //
        for j = 1:nb_couples
            if (p_cross>grand(1, 1, "def")) then
                [x1, x2] = crossover_func(Indiv1(j), Indiv2(j), param);
                Indiv1(j) = x1;
                Indiv2(j) = x2;
                ToCompute_I1(j) = %T;
                ToCompute_I2(j) = %T;
            else
                ToCompute_I1(j) = %F;
                ToCompute_I2(j) = %F;
            end
        end
        //
        // Mutation
        //
        for j = 1:nb_couples
            if (p_mut>grand(1, 1, "def")) then
                x1 = mutation_func(Indiv1(j), param);
                Indiv1(j) = x1;
                ToCompute_I1(j) = %T;
            end
            if (p_mut>grand(1, 1, "def")) then
                x2 = mutation_func(Indiv2(j), param);
                Indiv2(j) = x2;
                ToCompute_I2(j) = %T;
            end
        end
        //
        // Computation of the objective functions
		
		k=0;kk=0; // counters to iterate 
		for j = 1:nb_couples // for all couples in the population
			if ToCompute_I1(j) then// if to be computed
				k=k+1;
				if k==1 then // create the first vector of parameters
					[vec_param_pop1]=Indiv1(j);
				else // concatenate for parallel_run
					[vec_param_indiv1]=Indiv1(j);
					indices_indiv1(k)=j;
					vec_param_pop1=cat(2,vec_param_pop1,vec_param_indiv1);
				end
			end
			if ToCompute_I2(j) then// if to be computed
				kk=kk+1;
				if kk==1 then
					[vec_param_pop2]=Indiv2(j);
				else
					[vec_param_indiv2]=Indiv2(j);
					indices_indiv2(kk)=j;
					vec_param_pop2=cat(2,vec_param_pop2,vec_param_indiv2);
				end
			end
		end
		
		// Parallel_run
		//disp("Parallel launch for Indiv1...");
		[objectifs_Indiv1]=parallel_run(vec_param_pop1,_ga_f);
		objectifs_Indiv1=objectifs_Indiv1';
		//disp("Parallel launch for Indiv2...");
		[objectifs_Indiv2]=parallel_run(vec_param_pop2,_ga_f);
		objectifs_Indiv2=objectifs_Indiv2';
		
		// Updating indexes
		//disp("Updating FObj1 ...");
		for k=1:length(objectifs_Indiv1)
			if indices_indiv1(k)<> 0 then
				FObj_Indiv1(indices_indiv1(k))= objectifs_Indiv1(k);
			end;
		end
		for k=1:length(objectifs_Indiv2)
			if indices_indiv2(k)<> 0 then
				FObj_Indiv2(indices_indiv2(k))= objectifs_Indiv2(k);
			end;
		end

        // Reinit ToCompute lists
        ToCompute_I1 = ToCompute_I1 & %F;
        ToCompute_I2 = ToCompute_I2 & %F;
        // Recombination
        [Pop, FObj_Pop] = selection_func(Pop, Indiv1, Indiv2, FObj_Pop, FObj_Indiv1, FObj_Indiv2, [], [], [], param);
        // Callback for plotting / printing intermediate results or stopping the algorithm
        if (Log) then
            stop = output_func(i, nb_generation, Pop, FObj_Pop, param);
            if (stop) then
                break
            end
        end
    end

    pop_opt  = Pop;
    pop_opt  = codage_func(pop_opt, "decode", param);
    fobj_pop_opt = FObj_Pop;
endfunction

 

Running Energyplus in parallel on Windows

A very usefull shell script for a parallel run of EnergyPlus on Windows, using command line with Git-Bash (!) (for those who don't have Windows 10)

#!/bin/bash
##################################################################
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#|   _   ||   |  | ||   |___ |   |    
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#
##################################################################
# ce script en bash lance energypus en parallele 
#(tout est dans le "&" esperluette)                              #
##################################################################
# Last modification : 01/04/2018                                 #
##################################################################
# Copyright (C) 2018 Alexis SAUVAGEON                            #  
# This program is free software; you can redistribute it and/or  #
# modify it under the terms of the GNU General Public License    #
# as published by the Free Software Foundation; either version   #
# of the License, or (at your option) any later version.         #
##################################################################
# Contact : alexis[dot]sauvageon[at]arep[dot]com                                
##################################################################


cd ${0%/*} || exit 1 # un peu de magie au debut (tire de openfoam)
# define nbr cpus
ncpus=$1
ncpus=30

if [ -z "${1}" ]; then
    	ncpus=30
fi
# pour tout fichier "f" dans le dossier courant "*/"
for f in Trajectoire*/ 
do # faire :
	echo '----- lancement energyplus pour ' $f \n # ecrire qu'on commence avec le fichier "f"
	# lancer energyplus (meme cmd que dans python)
	g=${f::-1} # j'enleve le "/" final pour eviter sa redondance dans la ligne suivante avec celui de "/Simulation_*.idf"
	# on execute E+ pour tous les dossiers au meme niveau que le script :
	#		> IDF de chaque dossier 
	#		> EPW au meme niveau que le script
	exec C:/EnergyPlusV8-3-0/energyplus.exe -d $f -w ./in.epw $g/Simulation_*.idf &
	#		> on check si E+ tourne et on l'affiche dans le terminal
	process=$(ps cax | grep energyplus)
	if [ $? -eq 0 ]; then
		echo "Process is running."
	else
		echo "Process is not running."
	fi
	#		> on compte le nombre de processus actif E+
	numproc=$(ps -ef | grep -v grep | grep energyplus | wc -l)
	#		> tant que nbr E+ actif > ncpu autorise, on attend et on met a jour numproc
  while [[ $numproc -ge $ncpus ]]
  do
    sleep 5
    numproc=$(ps -ef | grep -v grep | grep energyplus | wc -l)
    echo $numproc 
  done
done