def solver(method: str, kp: Knapsack, items: List[Item], additionalArgs=None):
if method == 'Heuristic':
# Execute the heuristic method
simple_heuristic = SimpleHeuristic(additionalArgs)
return ConstructiveSolution(kp, items, simple_heuristic).getValue()
elif method == 'SimulatedAnnealing':
# Execute the Simulated Annealing metaheuristic
return solveMetaheuristic(method, kp, items, additionalArgs)
elif method == 'RandomSearch':
# Execute the Random Search metaheuristic
return solveMetaheuristic(method, kp, items, additionalArgs)
elif method == 'HyperheuristicNaive':
# Execute the Hyper-heuristic naive model
hh = HyperheuristicNaive(additionalArgs)
return ConstructiveSolution(kp, items, hh).getValue()
elif method == 'Hyperheuristic':
# Execute the Hyper-heuristic based on LSTM model
return hyperheuristicSolver(kp, items, additionalArgs).getValue()
elif method == 'Backtracking':
# Execute the recursive backtracking method
return kpBacktracking(kp.getCapacity(), items).getValue()
elif method == 'DP':
# Execute the dynamic programming method
return kpDP(kp.getCapacity(), items).getValue()
elif method == 'MILP':
# Execute the mixed integer linear programming method
return kpMILP(kp.getCapacity(), items).getValue()
elif method in list(heuristicComparison.keys()):
# Given the heuristic as method parameter, execute the constructive heuristic method
simple_heuristic = SimpleHeuristic(method)
return ConstructiveSolution(kp, items, simple_heuristic).getValue()
else:
return 0
def kpDP(W: int, items: List[Item]):
n = len(items)
A = np.zeros((n + 1, W + 1))
# Calculate the DP in the bottom-up fashion way
for idx, item in enumerate(items, start=1):
w, p = item.getWeight(), item.getProfit()
for Wi in range(W + 1):
if Wi == 0:
continue
elif idx == 0:
A[idx, Wi] = p if w <= Wi else 0
elif w <= Wi:
A[idx, Wi] = max(A[idx - 1, Wi], A[idx - 1, Wi - w] + p)
else:
A[idx, Wi] = A[idx - 1, Wi]
# Find the items that get the best solution
kp, idx = Knapsack(W), n
while idx >= 1 and kp.getCapacity() > 0:
if A[idx - 1, kp.getCapacity()] != A[idx, kp.getCapacity()]:
kp.pack(items[idx - 1])
idx -= 1
return kp
def solveMetaheuristic(method: str,
kp: Knapsack,
items: List[Item],
saveMetaheuristic=False,
fileName='traindata.csv',
overwrite=False):
W = kp.getCapacity()
items_copy = items.copy()
# Execute the chosen method
if method == 'SimulatedAnnealing':
kp, mh = SimulatedAnnealing(kp, items)
elif method == 'RandomSearch':
kp, mh = RandomSearch(kp, items)
else:
return 0
# Save the sequence of heuristics
if saveMetaheuristic:
mh.saveMetaheuristic(W, items_copy, fileName, overwrite)
return kp.getValue()