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  <h1>Source code for macop.algorithms.mono.IteratedLocalSearch</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Iterated Local Search Algorithm implementation</span>
<span class="sd">&quot;&quot;&quot;</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>
<span class="kn">from</span> <span class="nn">.LocalSearch</span> <span class="kn">import</span> <span class="n">LocalSearch</span>


<div class="viewcode-block" id="IteratedLocalSearch"><a class="viewcode-back" href="../../../../macop/macop.algorithms.mono.IteratedLocalSearch.html#macop.algorithms.mono.IteratedLocalSearch.IteratedLocalSearch">[docs]</a><span class="k">class</span> <span class="nc">IteratedLocalSearch</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Iterated Local Search used to avoir local optima and increave EvE (Exploration vs Exploitation) compromise</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 optimization 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">    &quot;&quot;&quot;</span>
<div class="viewcode-block" id="IteratedLocalSearch.run"><a class="viewcode-back" href="../../../../macop/macop.algorithms.mono.IteratedLocalSearch.html#macop.algorithms.mono.IteratedLocalSearch.IteratedLocalSearch.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="n">_ls_evaluations</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Run the iterated local search algorithm using local search (EvE compromise)</span>

<span class="sd">        Args:</span>
<span class="sd">            _evaluations: {int} -- number of global evaluations for ILS</span>
<span class="sd">            _ls_evaluations: {int} -- number of Local search evaluations (default: 100)</span>

<span class="sd">        Returns:</span>
<span class="sd">            {Solution} -- best solution found</span>
<span class="sd">        &quot;&quot;&quot;</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"># enable resuming for ILS</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">resume</span><span class="p">()</span>

        <span class="c1"># passing global evaluation param from ILS</span>
        <span class="n">ls</span> <span class="o">=</span> <span class="n">LocalSearch</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initializer</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">policy</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">validator</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">maximise</span><span class="p">,</span>
                         <span class="n">_parent</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span>

        <span class="c1"># add same callbacks</span>
        <span class="k">for</span> <span class="n">callback</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">callbacks</span><span class="p">:</span>
            <span class="n">ls</span><span class="o">.</span><span class="n">addCallback</span><span class="p">(</span><span class="n">callback</span><span class="p">)</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="c1"># create and search solution from local search</span>
            <span class="n">newSolution</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">_ls_evaluations</span><span class="p">)</span>

            <span class="c1"># if better solution than currently, replace it</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="c1"># number of evaluatins increased from LocalSearch</span>
            <span class="c1"># increase number of evaluations and progress are then not necessary there</span>
            <span class="c1">#self.increaseEvaluation()</span>
            <span class="c1">#self.progress()</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">information</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="s2">&quot;End of </span><span class="si">%s</span><span class="s2">, best solution found </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
                     <span class="p">(</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="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bestSolution</span><span class="p">))</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">end</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>

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