#region License Information
/* HeuristicLab
 * Author: Kaifeng Yang
 * 
 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *\
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */

// SMS-EMOA
/*
Implemenetation of a real-coded SMS_EMOA algorithm.
This implementation follows the description of: 'M. Emmerich, N. Beume, and B. Naujoks. 
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion.EMO 2005.'
*/
#endregion

using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Random;
using System.Linq;
using System;
using CancellationToken = System.Threading.CancellationToken;
using HeuristicLab.Analysis;
using HeuristicLab.Problems.TestFunctions.MultiObjective;
using HeuristicLab.Optimization;
using System.Collections.Generic;


/*  This algorithm is SMS-EMOA implementation on HL. The main structure and interfaces with HL are copied from MOEA/D on HL, which was written by Dr. Bogdan Burlacu. The S-metric selection operator was adapted from Kaifeng's MATLAB toolbox in SMS-EMOA. The computational complexity of HVC is AT LEAST $O (n^2 \log n)$ in 2-D and 3-D cases. HVC should definitely be reduced to $\Theta (n \times \log n)$. 
 *  
 *  This algorithm assumes: 
 *    1. minimization problems. For maximization problems, it is better to add "-" symbol.
 *  
 *  This algorithm works on:
 *    1. continuous, discrete, mixed-integer MOO problems. For different types of problems, the operators should be adjusted accordingly. 
 *    2. both multi-objective and many-objective problems. For many-objective problems, the bottleneck is the computational complexity of HV. 
 * 
 * This algorithm is the basic implementation of SMS-EMOA, proposed by Michael Emmerich et. al. Some potential improvements can be: 
 *    1. Dynamic reference point strategy
 *    2. Normalized fitness value strategy ---- desirability function. See, Yali, Longmei, Kaifeng, Michael Emmerich CEC paper. 
 *    3. HVC calculation should definitely be improved, at least in the 2D and 3D cases.
 *    4. multiple point strategy when $\lambda>1$ 
 *    5. multiple reference points strategy, in ICNC 2016, Zhiwei Yang et. al. 
 *    6. HVC approximation by R2 for MANY OBJECTIVE cases, by Ishibushi 2019, IEEE TEC  
 *    7. Maybe: See maps 
 * 
 * Global parameters:
 *    1. population  
 *    
 * Many thanks for Bogdan Burlacu and Johannes Karder, especially Bogdan for his explanation, help, and supports. 
 */

namespace HeuristicLab.Algorithms.DynamicALPS {
  // Format: 
  // The indexed name of the algorithm/item, Description of the algorithm/item in HL  
  [Item("DynamicALPS-MainLoop", "DynamicALPS-MainLoop implementation adapted from SMS-EMOA in HL.")]

  // Call "HeuristicLab.Core"
  // Define the category of this class. 
  [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 125)]

  // Call "HEAL.Attic"
  // Define GUID for this Class
  [StorableType("A7F33D16-3495-43E8-943C-8A919123F541")]

  public class DynamicALPSAlgorithm : DynamicALPSAlgorithmBase {
    public DynamicALPSAlgorithm() { }

    protected DynamicALPSAlgorithm(DynamicALPSAlgorithm original, Cloner cloner) : base(original, cloner) { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new DynamicALPSAlgorithm(this, cloner);
    }

    [StorableConstructor]
    protected DynamicALPSAlgorithm(StorableConstructorFlag deserializing) : base(deserializing) { }

    protected override void Run(CancellationToken cancellationToken) {
      if (previousExecutionState != ExecutionState.Paused) { // Call "base" class, DynamicALPSAlgorithmBase
        InitializeAlgorithm(cancellationToken);
      }


      var populationSize = PopulationSize.Value;
      bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();

      var crossover = Crossover;
      var crossoverProbability = CrossoverProbability.Value;
      var mutator = Mutator;
      var mutationProbability = MutationProbability.Value;
      var evaluator = Problem.Evaluator;
      var analyzer = Analyzer;
      var rand = RandomParameter.Value;


      var maximumEvaluatedSolutions = MaximumEvaluatedSolutions.Value;


      int lambda = 1;            // the size of offspring 


      int nLayerALPS = ALPSLayers.Value;
      int counterLayerALPS = 0;

      // IMPROVE: ........
      int[] ageGapArray = new int[] { 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 };
      int[] numberDiscard = new int[10];



      activeLayer = new bool[nLayerALPS];
      int[][] ageMatrix = new int[nLayerALPS][];      // layer * population size



      // cancellation token for the inner operations which should not be immediately cancelled
      var innerToken = new CancellationToken();


      // run some analyzer on each layer (for now calculate scatter plots )
      List<Tuple<int, ScatterPlotAnalyzer>> layerAnalyzers;


      // 12022020
      layerPopulation = new IDynamicALPSSolution[nLayerALPS][];
      layerOffspringPopulation = new IDynamicALPSSolution[nLayerALPS][];
      layerJointPopulation = new IDynamicALPSSolution[nLayerALPS][];
      layerDiscardPopulation = new IDynamicALPSSolution[nLayerALPS][];


      layerPopulation[counterLayerALPS] = new IDynamicALPSSolution[populationSize];
      // BUG: The size of offspring should vary in different layers!!!!
      layerOffspringPopulation[counterLayerALPS] = new IDynamicALPSSolution[lambda];
      layerDiscardPopulation[counterLayerALPS] = new IDynamicALPSSolution[populationSize];
      population.CopyTo(layerPopulation[counterLayerALPS], 0);

      activeLayer[counterLayerALPS] = true;
      for (int i = 0; i < nLayerALPS; i++) {
        if (i == 0) {
          activeLayer[i] = true;
        }
        else { activeLayer[i] = false; }
        numberDiscard[i] = 0;
      }

      // Mainloop 
      while (evaluatedSolutions < maximumEvaluatedSolutions && !cancellationToken.IsCancellationRequested) {
        for (int i = 0; i < nLayerALPS; i++) {            // loop for every layer
          int discardedIndividualIndex = 0;
          if (activeLayer[i] == true) {                   // check the layer is active or not. 
            evaluatedSolutions = SMSEMOA(populationSize, lambda, i);
            if (evaluatedSolutions >= maximumEvaluatedSolutions) { break; }           // check evaluation budget
            ageMatrix[i] = layerJointPopulation[i].Select(x => x.Age).ToArray();      // get age info of the current layer joint population 
            if (ageMatrix[i].Max() > ageGapArray[i]) {    // mature: moving 
              discardedIndividualIndex = ageMatrix[i].ToList().IndexOf(ageMatrix[i].Max());   // BUG when two individual has the same maximal age???? NOT POSSBILE IN SMS-EMOA 
              layerDiscardPopulation[i][numberDiscard[i]] = layerJointPopulation[i][discardedIndividualIndex];                    // move the individual to the next layer 
              layerJointPopulation[i].Where((x, idx) => idx != discardedIndividualIndex).ToArray().CopyTo(layerPopulation[i], 0); // discard the indivudal in the current layer  
              numberDiscard[i] += 1;
              if (activeLayer[i + 1] == false) {   // next layer is not active  // bug, if i == number of layer,  out of range .
                if (numberDiscard[i] == populationSize) {
                  InitializeLayer(i + 1, populationSize, lambda);  // initilizae the layerPopulation for the next layer && ACTIVE FLAGE
                  layerDiscardPopulation[i].CopyTo(layerPopulation[i + 1], 0);
                  numberDiscard[i] = 0;
                }
                else {
                  // number of matured individuals < population size in the next layer
                }
              }
              else {  // next layer is active
                layerPopulation[i+1].CopyTo(layerJointPopulation[i + 1], 0);
                layerJointPopulation[i].Where((x, idx) => idx == discardedIndividualIndex).ToArray().CopyTo(layerJointPopulation[i + 1], populationSize);
                SmetricSelection(lambda, i + 1); // AGE and HVC 
                numberDiscard[i] = 0;
              }
            }
            layerPopulation[i].CopyTo(population, 0);  // BUG: should copy all the active layers to population.
          }
          else {
            // Some thing wrong? lol nothing wrong here ^_^
          }
        }



        // run analyzer
        var analyze = executionContext.CreateChildOperation(analyzer, globalScope);
        ExecuteOperation(executionContext, innerToken, analyze);
        // update Pareto-front approximation sets
        UpdateParetoFronts();
        // Show some results in the GUI.
        Results.AddOrUpdateResult("IdealPoint", new DoubleArray(IdealPoint));
        Results.AddOrUpdateResult("NadirPoint", new DoubleArray(NadirPoint));
        Results.AddOrUpdateResult("Evaluated Solutions", new IntValue(evaluatedSolutions));

        var allLayerResults = new ResultCollection();
        Results.AddOrUpdateResult("LayerResults", allLayerResults);
        
        // run layer analyzers
        for (int i = 0; i < activeLayer.Length; ++i) {
          if (!activeLayer[i]) continue;
          var scope = new Scope($"Layer {i}");
          var layer = layerPopulation[i];
          var layerResults = new ResultCollection();
          allLayerResults.Add(new Result(scope.Name, layerResults));

          var problem = (MultiObjectiveTestFunctionProblem)Problem;
          //scope.Variables.Add(new Variable("Individuals", new ItemArray<IScope>(layer.Select(x => (IScope)x.Individual))));
          //scope.Variables.Add(new Variable("Qualities", new ItemArray<DoubleArray>(layer.Select(x => new DoubleArray(x.Qualities)))));
          scope.SubScopes.AddRange(layer.Select(x => (IScope)x.Individual));
          scope.Variables.Add(new Variable("Results", layerResults));
          scope.Variables.Add(new Variable("TestFunction", problem.TestFunction));
          var scatterPlotAnalyzer = new ScatterPlotAnalyzer();
          scatterPlotAnalyzer.IndividualsParameter.ActualName = "RealVector";
          var scattetPlotAnalyzerContext = executionContext.CreateChildOperation(scatterPlotAnalyzer, scope);
          ExecuteOperation(executionContext, CancellationToken.None, scattetPlotAnalyzerContext);
          scope.SubScopes.Clear();
          scope.Variables.Clear();
        }

        // Update globalScope
        globalScope.SubScopes.Replace(population.Select(x => (IScope)x.Individual));


        //// intilize the population for the next layer
        //counterLayerALPS += 1;
        //layerPopulation[counterLayerALPS] = new IDynamicALPSSolution[populationSize];
        //layerPopulation[counterLayerALPS-1].CopyTo(layerPopulation[counterLayerALPS], 0); // DETELTE DUBGU 
        //// BUG lambda should be different~~~~!!!!
        //layerOffspringPopulation[counterLayerALPS] = new IDynamicALPSSolution[lambda];
      }
    }
  }
}