Esempio n. 1
0
            public double Evaluate(IChromosome chromosome)
            {
                BinaryChromosome knapsackConfiguration = (BinaryChromosome)chromosome;

                int   length = knapsackConfiguration.Length;
                ulong value  = knapsackConfiguration.Value;

                for (int i = 0; i < length; i++)
                {
                    if ((value & TestBitsMask[i]) == 0)
                    {
                        _bag.RemoveItem(_items[i]);
                    }
                    else
                    {
                        _bag.InsertItem(_items[i]);
                    }
                }

                if (_bag.AcceptableWeight())
                {
                    return(_bag.ItemsCost());
                }
                return(0);
            }
Esempio n. 2
0
        public int Solve(KnapsackProblemModel problem)
        {
            Bag bag = new Bag(problem.BagCapacity);

            List <Item> items = problem.Items.ToList();

            int      count       = items.Count;
            BitArray possibility = new BitArray(count);

            int bestItemsCost = 0;

            while (true)
            {
                for (int i = 0; i < count; i++)
                {
                    //NEXT BIT WILL BE INVERTED
                    bool next = !possibility[i];
                    possibility[i] = next;

                    //IF NEW VALUE IS TRUE INSERT ITEM - IF FALSE REMOVE IT
                    if (next)
                    {
                        bag.InsertItem(items[i]);
                    }
                    else
                    {
                        bag.RemoveItem(items[i]);
                    }

                    //ONE IS SET - END
                    if (next)
                    {
                        break;
                    }
                }

                if (bag.IsEmpty())
                {
                    break;
                }

                if (bag.AcceptableWeight())
                {
                    int itemsCost = bag.ItemsCost();
                    if (itemsCost > bestItemsCost)
                    {
                        bestItemsCost = itemsCost;
                    }
                }
            }

            return(bestItemsCost);
        }
Esempio n. 3
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        public int Solve(KnapsackProblemModel problem)
        {
            List <Item> items = problem.Items.ToList();

            items.Sort(Item.CostToWeightRatioComparerDescending);

            Bag bag = new Bag(problem.BagCapacity);

            foreach (Item t in items)
            {
                bag.InsertItem(t);
                if (!bag.AcceptableWeight())
                {
                    bag.RemoveItem(t);
                }
            }

            return(bag.ItemsCost());
        }
Esempio n. 4
0
        public int RecursiveSolver(Bag bag, List <Item> items, int bestKnown)
        {
            if (items.Count > 0)
            {
                int bestItemsCost = bestKnown;

                List <Item> recList = new List <Item>(items);
                recList.RemoveAt(0);

                int sum = recList.Sum(item => item.Cost) + bag.ItemsCost();
                if (sum >= bestKnown)
                {
                    bestItemsCost = RecursiveSolver(bag, recList, bestKnown);
                }
                bag.InsertItem(items[0]);
                if (bag.AcceptableWeight())
                {
                    int itemsCost = bag.ItemsCost();
                    if (itemsCost > bestItemsCost)
                    {
                        bestItemsCost = itemsCost;
                    }

                    recList = new List <Item>(items);
                    recList.Remove(items[0]);
                    sum = recList.Sum(item => item.Cost) + bag.ItemsCost();
                    if (sum >= bestItemsCost)
                    {
                        bestItemsCost = RecursiveSolver(bag, recList, bestItemsCost);
                    }
                }
                bag.RemoveItem(items[0]);
                return(bestItemsCost);
            }
            return(bestKnown);
        }