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  <h1>Source code for macop.algorithms.multi.MOEAD</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Multi-Ojective Evolutionary Algorithm with Scalar Decomposition algorithm</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="c1"># main imports</span>
<span class="kn">import</span> <span class="nn">pkgutil</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">...utils.color</span> <span class="kn">import</span> <span class="n">macop_text</span><span class="p">,</span> <span class="n">macop_line</span><span class="p">,</span> <span class="n">macop_progress</span>
<span class="kn">from</span> <span class="nn">...utils.modules</span> <span class="kn">import</span> <span class="n">load_class</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">.MOSubProblem</span> <span class="kn">import</span> <span class="n">MOSubProblem</span>

<span class="c1"># import all available solutions</span>
<span class="k">for</span> <span class="n">loader</span><span class="p">,</span> <span class="n">module_name</span><span class="p">,</span> <span class="n">is_pkg</span> <span class="ow">in</span> <span class="n">pkgutil</span><span class="o">.</span><span class="n">walk_packages</span><span class="p">(</span>
        <span class="n">path</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;macop/solutions&#39;</span><span class="p">],</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;macop.solutions.&#39;</span><span class="p">):</span>
    <span class="n">_module</span> <span class="o">=</span> <span class="n">loader</span><span class="o">.</span><span class="n">find_module</span><span class="p">(</span><span class="n">module_name</span><span class="p">)</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="n">module_name</span><span class="p">)</span>
    <span class="nb">globals</span><span class="p">()[</span><span class="n">module_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">_module</span>


<span class="k">def</span> <span class="nf">moEvaluator</span><span class="p">(</span><span class="n">_solution</span><span class="p">,</span> <span class="n">_evaluator</span><span class="p">,</span> <span class="n">_weights</span><span class="p">):</span>

    <span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="nb">eval</span><span class="p">(</span><span class="n">_solution</span><span class="p">)</span> <span class="k">for</span> <span class="nb">eval</span> <span class="ow">in</span> <span class="n">_evaluator</span><span class="p">]</span>

    <span class="c1"># associate objectives scores to solution</span>
    <span class="n">_solution</span><span class="o">.</span><span class="n">scores</span> <span class="o">=</span> <span class="n">scores</span>

    <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_weights</span><span class="p">)])</span>


<div class="viewcode-block" id="MOEAD"><a class="viewcode-back" href="../../../../macop/macop.algorithms.multi.MOEAD.html#macop.algorithms.multi.MOEAD.MOEAD">[docs]</a><span class="k">class</span> <span class="nc">MOEAD</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Multi-Ojective Evolutionary Algorithm with Scalar Decomposition</span>

<span class="sd">    Attributes:</span>
<span class="sd">        mu: {int} -- number of sub problems</span>
<span class="sd">        T: {[float]} -- number of neightbors for each sub problem</span>
<span class="sd">        nObjectives: {int} -- number of objectives (based of number evaluator)</span>
<span class="sd">        initalizer: {function} -- basic function strategy to initialize solution</span>
<span class="sd">        evaluator: {[function]} -- list of basic function in order to obtained fitness (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">        population: [{Solution}] -- population of solution, one for each sub problem</span>
<span class="sd">        pfPop: [{Solution}] -- pareto front population</span>
<span class="sd">        weights: [[{float}]] -- random weights used for custom mu sub problems</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>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                 <span class="n">_mu</span><span class="p">,</span>
                 <span class="n">_T</span><span class="p">,</span>
                 <span class="n">_initalizer</span><span class="p">,</span>
                 <span class="n">_evaluator</span><span class="p">,</span>
                 <span class="n">_operators</span><span class="p">,</span>
                 <span class="n">_policy</span><span class="p">,</span>
                 <span class="n">_validator</span><span class="p">,</span>
                 <span class="n">_maximise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                 <span class="n">_parent</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>

        <span class="c1"># redefinition of constructor to well use `initRun` method</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initializer</span> <span class="o">=</span> <span class="n">_initalizer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span> <span class="o">=</span> <span class="n">_evaluator</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">operators</span> <span class="o">=</span> <span class="n">_operators</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">=</span> <span class="n">_policy</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">validator</span> <span class="o">=</span> <span class="n">_validator</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">callbacks</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="c1"># by default</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">numberOfEvaluations</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">maxEvaluations</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nObjectives</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_evaluator</span><span class="p">)</span>

        <span class="c1"># other parameters</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">parent</span> <span class="o">=</span> <span class="n">_parent</span>  <span class="c1"># parent algorithm if it&#39;s sub algorithm</span>

        <span class="c1">#self.maxEvaluations = 0 # by default</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">maximise</span> <span class="o">=</span> <span class="n">_maximise</span>

        <span class="c1"># track reference of algo into operator (keep an eye into best solution)</span>
        <span class="k">for</span> <span class="n">operator</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">:</span>
            <span class="n">operator</span><span class="o">.</span><span class="n">setAlgo</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>

        <span class="c1"># also track reference for policy</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">policy</span><span class="o">.</span><span class="n">setAlgo</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">_mu</span> <span class="o">&lt;</span> <span class="n">_T</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`mu` cannot be less than `T`&#39;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">=</span> <span class="n">_mu</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">=</span> <span class="n">_T</span>

        <span class="c1"># initialize neighbors for each sub problem</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">setNeighbors</span><span class="p">()</span>

        <span class="n">weights</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nObjectives</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>

            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>
                <span class="n">angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
                <span class="n">weights</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angle</span><span class="p">),</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angle</span><span class="p">)])</span>

        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">nObjectives</span> <span class="o">&gt;=</span> <span class="mi">3</span><span class="p">:</span>

            <span class="c1"># random weights using uniform</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>
                <span class="n">w_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nObjectives</span><span class="p">)</span>
                <span class="n">weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">w_i</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">w_i</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unvalid number of objectives&#39;</span><span class="p">)</span>

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

        <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>

            <span class="c1"># compute weight sum from solution</span>
            <span class="n">sub_evaluator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">_solution</span><span class="p">:</span> <span class="n">moEvaluator</span><span class="p">(</span>
                <span class="n">_solution</span><span class="p">,</span> <span class="n">_evaluator</span><span class="p">,</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

            <span class="c1"># intialize each sub problem</span>
            <span class="n">subProblem</span> <span class="o">=</span> <span class="n">MOSubProblem</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">_initalizer</span><span class="p">,</span>
                                      <span class="n">sub_evaluator</span><span class="p">,</span> <span class="n">_operators</span><span class="p">,</span> <span class="n">_policy</span><span class="p">,</span>
                                      <span class="n">_validator</span><span class="p">,</span> <span class="n">_maximise</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subProblem</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">population</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">)]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pfPop</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="c1"># ref point based on number of evaluators</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">maximise</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refPoint</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</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="bp">self</span><span class="o">.</span><span class="n">nObjectives</span><span class="p">)]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">refPoint</span> <span class="o">=</span> <span class="p">[</span>
                <span class="n">sys</span><span class="o">.</span><span class="n">float_info</span><span class="o">.</span><span class="n">max</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="bp">self</span><span class="o">.</span><span class="n">nObjectives</span><span class="p">)</span>
            <span class="p">]</span>

<div class="viewcode-block" id="MOEAD.initRun"><a class="viewcode-back" href="../../../../macop/macop.algorithms.multi.MOEAD.html#macop.algorithms.multi.MOEAD.MOEAD.initRun">[docs]</a>    <span class="k">def</span> <span class="nf">initRun</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Method which initialiazes or re-initializes the whole algorithm context specifically for MOEAD</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># initialization is done during run method</span>
        <span class="k">pass</span></div>

<div class="viewcode-block" id="MOEAD.run"><a class="viewcode-back" href="../../../../macop/macop.algorithms.multi.MOEAD.html#macop.algorithms.multi.MOEAD.MOEAD.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">&quot;&quot;&quot;</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">        &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"># initialize each sub problem</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">population</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bestSolution</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">pfPop</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bestSolution</span><span class="p">)</span>

        <span class="c1"># enable callback resume for MOEAD</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">resume</span><span class="p">()</span>

        <span class="c1"># MOEAD 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">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>

                <span class="c1"># run 1 iteration into sub problem `i`</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
                <span class="n">spBestSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bestSolution</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">updateRefPoint</span><span class="p">(</span><span class="n">spBestSolution</span><span class="p">)</span>

                <span class="c1"># for each neighbor of current sub problem update solution if better</span>
                <span class="n">improvment</span> <span class="o">=</span> <span class="kc">False</span>
                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
                    <span class="k">if</span> <span class="n">spBestSolution</span><span class="o">.</span><span class="n">fitness</span><span class="p">(</span>
                    <span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">bestSolution</span><span class="o">.</span><span class="n">fitness</span><span class="p">():</span>

                        <span class="c1"># create new solution based on current new if better, computes fitness associated to new solution for sub problem</span>
                        <span class="n">class_name</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">spBestSolution</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span>

                        <span class="c1"># dynamically load solution class if unknown</span>
                        <span class="k">if</span> <span class="n">class_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
                            <span class="n">load_class</span><span class="p">(</span><span class="n">class_name</span><span class="p">,</span> <span class="nb">globals</span><span class="p">())</span>

                        <span class="n">newSolution</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span>
                            <span class="nb">globals</span><span class="p">()[</span><span class="s1">&#39;macop.solutions.&#39;</span> <span class="o">+</span> <span class="n">class_name</span><span class="p">],</span>
                            <span class="n">class_name</span><span class="p">)(</span><span class="n">spBestSolution</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
                                        <span class="nb">len</span><span class="p">(</span><span class="n">spBestSolution</span><span class="o">.</span><span class="n">data</span><span class="p">))</span>

                        <span class="c1"># evaluate solution for new sub problem and update as best solution</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">subProblems</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">evaluate</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">subProblems</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">bestSolution</span> <span class="o">=</span> <span class="n">newSolution</span>

                        <span class="c1"># update population solution for this sub problem</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">population</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">newSolution</span>

                        <span class="n">improvment</span> <span class="o">=</span> <span class="kc">True</span>

                <span class="c1"># add new solution if improvment is idenfity</span>
                <span class="k">if</span> <span class="n">improvment</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">pfPop</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spBestSolution</span><span class="p">)</span>

                <span class="c1"># update pareto front</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">pfPop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">paretoFront</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pfPop</span><span class="p">)</span>

                <span class="c1"># add progress here</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">progress</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="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">population</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">pfPop</span></div>

<div class="viewcode-block" id="MOEAD.progress"><a class="viewcode-back" href="../../../../macop/macop.algorithms.multi.MOEAD.html#macop.algorithms.multi.MOEAD.MOEAD.progress">[docs]</a>    <span class="k">def</span> <span class="nf">progress</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Log progress and apply callbacks if necessary</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">callbacks</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</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">callback</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>

        <span class="n">macop_progress</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">getGlobalEvaluation</span><span class="p">(),</span>
                       <span class="bp">self</span><span class="o">.</span><span class="n">getGlobalMaxEvaluation</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;-- </span><span class="si">%s</span><span class="s2"> evaluation </span><span class="si">%s</span><span class="s2"> of </span><span class="si">%s</span><span class="s2"> (</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">numberOfEvaluations</span><span class="p">,</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">maxEvaluations</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{0:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">numberOfEvaluations</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">maxEvaluations</span> <span class="o">*</span> <span class="mf">100.</span><span class="p">)))</span></div>

    <span class="k">def</span> <span class="nf">setNeighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>

        <span class="n">dmin</span> <span class="o">=</span> <span class="n">dmax</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">dmin</span> <span class="o">=</span> <span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
            <span class="n">dmax</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">dmin</span> <span class="o">=</span> <span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
            <span class="n">dmax</span> <span class="o">=</span> <span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span> <span class="o">/</span> <span class="mi">2</span>

        <span class="c1"># init neighbord list</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">)]</span>

        <span class="k">for</span> <span class="n">direction</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">dmin</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">direction</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">direction</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">dmin</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">-</span> <span class="n">dmax</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">direction</span> <span class="o">+</span> <span class="n">dmin</span><span class="p">,</span> <span class="n">direction</span> <span class="o">+</span> <span class="n">dmax</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">direction</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">direction</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">-</span> <span class="n">dmax</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">direction</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">updateRefPoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_solution</span><span class="p">):</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">maximise</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</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="k">if</span> <span class="n">_solution</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">refPoint</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">refPoint</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">_solution</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</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="k">if</span> <span class="n">_solution</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">refPoint</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">refPoint</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">_solution</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>

    <span class="k">def</span> <span class="nf">paretoFront</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_population</span><span class="p">):</span>

        <span class="n">paFront</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">indexes</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">nObjectives</span> <span class="o">=</span> <span class="nb">len</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="n">nSolutions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_population</span><span class="p">)</span>

        <span class="c1"># find dominated solution</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nSolutions</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nSolutions</span><span class="p">):</span>

                <span class="k">if</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">indexes</span><span class="p">:</span>
                    <span class="k">continue</span>

                <span class="n">nDominated</span> <span class="o">=</span> <span class="mi">0</span>

                <span class="c1"># check number of dominated objectives of current solution by the others solution</span>
                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">maximise</span><span class="p">:</span>
                        <span class="k">if</span> <span class="n">_population</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">_population</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
                            <span class="n">nDominated</span> <span class="o">+=</span> <span class="mi">1</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="k">if</span> <span class="n">_population</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">_population</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">scores</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
                            <span class="n">nDominated</span> <span class="o">+=</span> <span class="mi">1</span>

                <span class="k">if</span> <span class="n">nDominated</span> <span class="o">==</span> <span class="n">nObjectives</span><span class="p">:</span>
                    <span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
                    <span class="k">break</span>

        <span class="c1"># store the non dominated solution into pareto front</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nSolutions</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">indexes</span><span class="p">:</span>
                <span class="n">paFront</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_population</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">paFront</span>

<div class="viewcode-block" id="MOEAD.end"><a class="viewcode-back" href="../../../../macop/macop.algorithms.multi.MOEAD.html#macop.algorithms.multi.MOEAD.MOEAD.end">[docs]</a>    <span class="k">def</span> <span class="nf">end</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Display end message into `run` method</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="nb">print</span><span class="p">(</span>
            <span class="n">macop_text</span><span class="p">(</span><span class="s1">&#39;(</span><span class="si">{}</span><span class="s1">) Found after </span><span class="si">{}</span><span class="s1"> evaluations&#39;</span><span class="o">.</span><span class="n">format</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">numberOfEvaluations</span><span class="p">)))</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">solution</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pfPop</span><span class="p">):</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  - [</span><span class="si">{}</span><span class="s1">] </span><span class="si">{}</span><span class="s1"> : </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">solution</span><span class="o">.</span><span class="n">scores</span><span class="p">,</span> <span class="n">solution</span><span class="p">))</span>

        <span class="nb">print</span><span class="p">(</span><span class="n">macop_line</span><span class="p">())</span></div>

    <span class="k">def</span> <span class="nf">information</span><span class="p">(</span><span class="bp">self</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;-- Pareto front :&quot;</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">solution</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pfPop</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;-- </span><span class="si">%s</span><span class="s2">] SCORE </span><span class="si">%s</span><span class="s2"> - </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
                         <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">solution</span><span class="o">.</span><span class="n">scores</span><span class="p">,</span> <span class="n">solution</span><span class="p">))</span>

    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> using </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="nb">type</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">population</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
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