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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-07 09:06:44 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-07 09:06:44 +0000
commited5640d8b587fbcfed7dd7967f3de04b37a76f26 (patch)
tree7a5f7c6c9d02226d7471cb3cc8fbbf631b415303 /sccomp/source/solver/ParticelSwarmOptimization.hxx
parentInitial commit. (diff)
downloadlibreoffice-ed5640d8b587fbcfed7dd7967f3de04b37a76f26.tar.xz
libreoffice-ed5640d8b587fbcfed7dd7967f3de04b37a76f26.zip
Adding upstream version 4:7.4.7.upstream/4%7.4.7upstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'sccomp/source/solver/ParticelSwarmOptimization.hxx')
-rw-r--r--sccomp/source/solver/ParticelSwarmOptimization.hxx177
1 files changed, 177 insertions, 0 deletions
diff --git a/sccomp/source/solver/ParticelSwarmOptimization.hxx b/sccomp/source/solver/ParticelSwarmOptimization.hxx
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+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ */
+
+#pragma once
+
+#include <vector>
+#include <random>
+#include <limits>
+
+struct Particle
+{
+ Particle(size_t nDimensionality)
+ : mVelocity(nDimensionality)
+ , mPosition(nDimensionality)
+ , mCurrentFitness(std::numeric_limits<float>::lowest())
+ , mBestPosition(nDimensionality)
+ , mBestFitness(std::numeric_limits<float>::lowest())
+ {
+ }
+
+ std::vector<double> mVelocity;
+
+ std::vector<double> mPosition;
+ double mCurrentFitness;
+
+ std::vector<double> mBestPosition;
+ double mBestFitness;
+};
+
+template <typename DataProvider> class ParticleSwarmOptimizationAlgorithm
+{
+private:
+ // inertia
+ static constexpr double constWeight = 0.729;
+ // cognitive coefficient
+ static constexpr double c1 = 1.49445;
+ // social coefficient
+ static constexpr double c2 = 1.49445;
+
+ static constexpr double constAcceptedPrecision = 0.000000001;
+
+ DataProvider& mrDataProvider;
+
+ size_t mnNumOfParticles;
+
+ std::vector<Particle> maSwarm;
+
+ std::random_device maRandomDevice;
+ std::mt19937 maGenerator;
+ size_t mnDimensionality;
+
+ std::uniform_real_distribution<> maRandom01;
+
+ std::vector<double> maBestPosition;
+ double mfBestFitness;
+ int mnGeneration;
+ int mnLastChange;
+
+public:
+ ParticleSwarmOptimizationAlgorithm(DataProvider& rDataProvider, size_t nNumOfParticles)
+ : mrDataProvider(rDataProvider)
+ , mnNumOfParticles(nNumOfParticles)
+ , maGenerator(maRandomDevice())
+ , mnDimensionality(mrDataProvider.getDimensionality())
+ , maRandom01(0.0, 1.0)
+ , maBestPosition(mnDimensionality)
+ , mfBestFitness(std::numeric_limits<float>::lowest())
+ , mnGeneration(0)
+ , mnLastChange(0)
+ {
+ }
+
+ std::vector<double> const& getResult() { return maBestPosition; }
+
+ int getGeneration() { return mnGeneration; }
+
+ int getLastChange() { return mnLastChange; }
+
+ void initialize()
+ {
+ mnGeneration = 0;
+ mnLastChange = 0;
+ maSwarm.clear();
+
+ mfBestFitness = std::numeric_limits<float>::lowest();
+
+ maSwarm.reserve(mnNumOfParticles);
+ for (size_t i = 0; i < mnNumOfParticles; i++)
+ {
+ maSwarm.emplace_back(mnDimensionality);
+ Particle& rParticle = maSwarm.back();
+
+ mrDataProvider.initializeVariables(rParticle.mPosition, maGenerator);
+ mrDataProvider.initializeVariables(rParticle.mVelocity, maGenerator);
+
+ for (size_t k = 0; k < mnDimensionality; k++)
+ {
+ rParticle.mPosition[k] = mrDataProvider.clampVariable(k, rParticle.mPosition[k]);
+ }
+
+ rParticle.mCurrentFitness = mrDataProvider.calculateFitness(rParticle.mPosition);
+
+ for (size_t k = 0; k < mnDimensionality; k++)
+ {
+ rParticle.mPosition[k] = mrDataProvider.clampVariable(k, rParticle.mPosition[k]);
+ }
+
+ rParticle.mBestPosition.insert(rParticle.mBestPosition.begin(),
+ rParticle.mPosition.begin(), rParticle.mPosition.end());
+ rParticle.mBestFitness = rParticle.mCurrentFitness;
+
+ if (rParticle.mCurrentFitness > mfBestFitness)
+ {
+ mfBestFitness = rParticle.mCurrentFitness;
+ maBestPosition.insert(maBestPosition.begin(), rParticle.mPosition.begin(),
+ rParticle.mPosition.end());
+ }
+ }
+ }
+
+ bool next()
+ {
+ bool bBestChanged = false;
+
+ for (Particle& rParticle : maSwarm)
+ {
+ double fRandom1 = maRandom01(maGenerator);
+ double fRandom2 = maRandom01(maGenerator);
+
+ for (size_t k = 0; k < mnDimensionality; k++)
+ {
+ rParticle.mVelocity[k]
+ = (constWeight * rParticle.mVelocity[k])
+ + (c1 * fRandom1 * (rParticle.mBestPosition[k] - rParticle.mPosition[k]))
+ + (c2 * fRandom2 * (maBestPosition[k] - rParticle.mPosition[k]));
+
+ mrDataProvider.clampVariable(k, rParticle.mVelocity[k]);
+
+ rParticle.mPosition[k] += rParticle.mVelocity[k];
+ rParticle.mPosition[k] = mrDataProvider.clampVariable(k, rParticle.mPosition[k]);
+ }
+
+ rParticle.mCurrentFitness = mrDataProvider.calculateFitness(rParticle.mPosition);
+
+ if (rParticle.mCurrentFitness > rParticle.mBestFitness)
+ {
+ rParticle.mBestFitness = rParticle.mCurrentFitness;
+ rParticle.mBestPosition.insert(rParticle.mBestPosition.begin(),
+ rParticle.mPosition.begin(),
+ rParticle.mPosition.end());
+ }
+
+ if (rParticle.mCurrentFitness > mfBestFitness)
+ {
+ if (std::abs(rParticle.mCurrentFitness - mfBestFitness) > constAcceptedPrecision)
+ {
+ bBestChanged = true;
+ mnLastChange = mnGeneration;
+ }
+ maBestPosition.insert(maBestPosition.begin(), rParticle.mPosition.begin(),
+ rParticle.mPosition.end());
+ mfBestFitness = rParticle.mCurrentFitness;
+ }
+ }
+ mnGeneration++;
+ return bBestChanged;
+ }
+};
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */