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<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>
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