update TSEA
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@@ -527,7 +527,7 @@ void C_AO_TSEA::Revision ()
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stepF = (fB - minFval) / vClusters;
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//3. Разметка по вертикали дочерней популяции---------------------------------
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//Разметка по вертикали дочерней популяции------------------------------------
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for (int i = 0; i < popSize; i++)
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{
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if (agent [i].f == fB) agent [i].labelClustV = vClusters - 1;
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@@ -571,11 +571,11 @@ void C_AO_TSEA::Revision ()
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{
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agent [i].label = km.KNN (data, agent [i], neighbNumb, hClusters);
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}
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/*
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if (epochsNow % 5 == 0)
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if (epochsNow % 50 == 0)
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{
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km.KMeansPlusPlusInit (data, ArraySize (data), clusters);
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km.KMeans (data, ArraySize (data), clusters);
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//km.KMeansPlusPlusInit (data, ArraySize (data), clusters);
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//km.KMeans (data, ArraySize (data), clusters);
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for (int v = 0; v < vClusters; v++)
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{
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@@ -603,7 +603,6 @@ void C_AO_TSEA::Revision ()
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cell [v].cell [h].agent [size - 1] = data [i];
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}
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}
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*/
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}
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//5, 10. Поместить популяцию в панцирь----------------------------------------
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@@ -624,20 +623,30 @@ void C_AO_TSEA::Revision ()
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for (int c = 0; c < maxAgentsInCell; c++)
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{
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if (agent [i].f < minF)
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if (cell [v].cell [h].agent [c].f < minF)
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{
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minF = agent [i].f;
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minF = cell [v].cell [h].agent [c].f;
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posMin = c;
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}
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if (agent [i].f > maxF)
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if (cell [v].cell [h].agent [c].f > maxF)
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{
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maxF = agent [i].f;
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maxF = cell [v].cell [h].agent [c].f;
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posMax = c;
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}
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}
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if (v == 0) pos = posMax;
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else pos = posMin;
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if (v == 0)
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{
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if (agent [i].f < minF)
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{
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pos = posMin;
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}
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else
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{
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pos = posMax;
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}
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}
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else pos = posMin;
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}
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else
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{
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@@ -8,12 +8,12 @@ A list of implemented (+) optimization algorithms, (-) not included in the repos
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-DE (differential evolution)
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+BSA (bird swarm algorithm)
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-HS (harmony search)
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-SSG (saplings sowing and growing)
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-SSG (saplings sowing and growing)
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+TSEA (turtle shell evolution algorithm)
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-(PO)ES ((PO) evolution strategies)
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+BSO (brain storm optimization)
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+WOAm (wale optimization algorithm M)
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-ACOm (ant colony optimization M)
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+TSEA (turtle shell evolution algorithm)
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-ACOm (ant colony optimization M)
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-BFO-GA (bacterial foraging optimization - ga)
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-MEC (mind evolutionary computation)
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-IWO (invasive weed optimization)
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