<!DOCTYPE html> <!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]--> <!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]--> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>macop.algorithms.mono.HillClimberFirstImprovment — macop v1.0.5 documentation</title> <script type="text/javascript" src="../../../../_static/js/modernizr.min.js"></script> <script type="text/javascript" id="documentation_options" data-url_root="../../../../" src="../../../../_static/documentation_options.js"></script> <script type="text/javascript" src="../../../../_static/jquery.js"></script> <script type="text/javascript" src="../../../../_static/underscore.js"></script> <script type="text/javascript" src="../../../../_static/doctools.js"></script> <script type="text/javascript" src="../../../../_static/language_data.js"></script> <script type="text/javascript" src="../../../../_static/js/theme.js"></script> <link 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class="caption"><span class="caption-text">Contents:</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="../../../../description.html">Description</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../../macop.html">Documentation</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../../examples.html">Some examples</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../../contributing.html">Contributing</a></li> </ul> </div> </div> </nav> <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"> <nav class="wy-nav-top" aria-label="top navigation"> <i data-toggle="wy-nav-top" class="fa fa-bars"></i> <a href="../../../../index.html">macop</a> </nav> <div class="wy-nav-content"> <div class="rst-content"> <div role="navigation" aria-label="breadcrumbs navigation"> <ul class="wy-breadcrumbs"> <li><a href="../../../../index.html">Docs</a> »</li> <li><a href="../../../index.html">Module code</a> »</li> <li>macop.algorithms.mono.HillClimberFirstImprovment</li> <li class="wy-breadcrumbs-aside"> </li> </ul> <hr/> </div> <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article"> <div itemprop="articleBody"> <h1>Source code for macop.algorithms.mono.HillClimberFirstImprovment</h1><div class="highlight"><pre> <span></span><span class="sd">"""Hill Climber First Improvment algorithm starting from new solution and explore using neighborhood and loop over the best one obtained from neighborhood search space</span> <span class="sd">"""</span> <span class="c1"># main imports</span> <span class="kn">import</span> <span class="nn">logging</span> <span class="c1"># module imports</span> <span class="kn">from</span> <span class="nn">..Algorithm</span> <span class="kn">import</span> <span class="n">Algorithm</span> <div class="viewcode-block" id="HillClimberFirstImprovment"><a class="viewcode-back" href="../../../../macop/macop.algorithms.mono.HillClimberFirstImprovment.html#macop.algorithms.mono.HillClimberFirstImprovment.HillClimberFirstImprovment">[docs]</a><span class="k">class</span> <span class="nc">HillClimberFirstImprovment</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span> <span class="sd">"""Hill Climber First Improvment used as quick exploration optimisation algorithm</span> <span class="sd"> This algorithm do a neighborhood exploration of a new generated solution (by doing operation on the current solution obtained) in order to find a better solution from the neighborhood space.</span> <span class="sd"> Then replace the current solution by the first one from the neighbordhood space which is better than the current solution.</span> <span class="sd"> Do these steps until a number of evaluation (stopping criterion) is reached.</span> <span class="sd"> Attributes:</span> <span class="sd"> initalizer: {function} -- basic function strategy to initialize solution</span> <span class="sd"> evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives)</span> <span class="sd"> operators: {[Operator]} -- list of operator to use when launching algorithm</span> <span class="sd"> policy: {Policy} -- Policy class implementation strategy to select operators</span> <span class="sd"> validator: {function} -- basic function to check if solution is valid or not under some constraints</span> <span class="sd"> maximise: {bool} -- specify kind of optimisation problem </span> <span class="sd"> currentSolution: {Solution} -- current solution managed for current evaluation</span> <span class="sd"> bestSolution: {Solution} -- best solution found so far during running algorithm</span> <span class="sd"> callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm</span> <span class="sd"> """</span> <div class="viewcode-block" id="HillClimberFirstImprovment.run"><a class="viewcode-back" href="../../../../macop/macop.algorithms.mono.HillClimberFirstImprovment.html#macop.algorithms.mono.HillClimberFirstImprovment.HillClimberFirstImprovment.run">[docs]</a> <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">evaluations</span><span class="p">):</span> <span class="sd">"""</span> <span class="sd"> Run the local search algorithm</span> <span class="sd"> Args:</span> <span class="sd"> evaluations: {int} -- number of Local search evaluations</span> <span class="sd"> </span> <span class="sd"> Returns:</span> <span class="sd"> {Solution} -- best solution found</span> <span class="sd"> """</span> <span class="c1"># by default use of mother method to initialize variables</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">evaluations</span><span class="p">)</span> <span class="c1"># initialize current solution and best solution</span> <span class="bp">self</span><span class="o">.</span><span class="n">initRun</span><span class="p">()</span> <span class="n">solutionSize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span><span class="o">.</span><span class="n">_size</span> <span class="c1"># local search algorithm implementation</span> <span class="k">while</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">():</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">solutionSize</span><span class="p">):</span> <span class="c1"># update current solution using policy</span> <span class="n">newSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span><span class="p">)</span> <span class="c1"># if better solution than currently, replace it and stop current exploration (first improvment)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isBetter</span><span class="p">(</span><span class="n">newSolution</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span> <span class="o">=</span> <span class="n">newSolution</span> <span class="k">break</span> <span class="c1"># increase number of evaluations</span> <span class="bp">self</span><span class="o">.</span><span class="n">increaseEvaluation</span><span class="p">()</span> <span class="bp">self</span><span class="o">.</span><span class="n">progress</span><span class="p">()</span> <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"---- Current </span><span class="si">{</span><span class="n">newSolution</span><span class="si">}</span><span class="s2"> - SCORE </span><span class="si">{</span><span class="n">newSolution</span><span class="o">.</span><span class="n">fitness</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="c1"># stop algorithm if necessary</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">():</span> <span class="k">break</span> <span class="c1"># set new current solution using best solution found in this neighbor search</span> <span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span> <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"End of </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">, best solution found </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span></div></div> </pre></div> </div> </div> <footer> <hr/> <div role="contentinfo"> <p> © Copyright 2020, Jérôme BUISINE </p> </div> Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a 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