multiobj_c + + . cpp
/*版权2018,Gurobi优化，L狗万app足彩LC */ /*想涵盖三个不同的集合，但受*元素的共同预算允许使用。然而，这些集合有不同的优先级*被覆盖;我们用多目标优化来解决这个问题。*/ #include "gurobi_c++.h" #include #include using namespace std;int main(void) {GRBEnv *env = 0;GRBVar *Elem = 0;int e, i, status, nSolutions;try{//样本数据const int groundSetSize = 20;const int nSubsets = 4;const int预算= 12; double Set[][20] = { { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 }, { 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0 } }; int SetObjPriority[] = {3, 2, 2, 1}; double SetObjWeight[] = {1.0, 0.25, 1.25, 1.0}; // Create environment env = new GRBEnv("multiobj_c++.log"); // Create initial model GRBModel model = GRBModel(*env); model.set(GRB_StringAttr_ModelName, "multiobj_c++"); // Initialize decision variables for ground set: // x[e] == 1 if element e is chosen for the covering. Elem = model.addVars(groundSetSize, GRB_BINARY); for (e = 0; e < groundSetSize; e++) { ostringstream vname; vname << "El" << e; Elem[e].set(GRB_StringAttr_VarName, vname.str()); } // Constraint: limit total number of elements to be picked to be at most // Budget GRBLinExpr lhs; lhs = 0; for (e = 0; e < groundSetSize; e++) { lhs += Elem[e]; } model.addConstr(lhs <= Budget, "Budget"); // Set global sense for ALL objectives model.set(GRB_IntAttr_ModelSense, GRB_MAXIMIZE); // Limit how many solutions to collect model.set(GRB_IntParam_PoolSolutions, 100); // Set and configure i-th objective for (i = 0; i < nSubsets; i++) { GRBLinExpr objn = 0; for (e = 0; e < groundSetSize; e++) objn += Set[i][e]*Elem[e]; ostringstream vname; vname << "Set" << i; model.setObjectiveN(objn, i, SetObjPriority[i], SetObjWeight[i], 1.0 + i, 0.01, vname.str()); } // Save problem model.write("multiobj_c++.lp"); // Optimize model.optimize(); // Status checking status = model.get(GRB_IntAttr_Status); if (status == GRB_INF_OR_UNBD || status == GRB_INFEASIBLE || status == GRB_UNBOUNDED ) { cout << "The model cannot be solved " << "because it is infeasible or unbounded" << endl; return 1; } if (status != GRB_OPTIMAL) { cout << "Optimization was stopped with status " << status << endl; return 1; } // Print best selected set cout << "Selected elements in best solution:" << endl << "\t"; for (e = 0; e < groundSetSize; e++) { if (Elem[e].get(GRB_DoubleAttr_X) < .9) continue; cout << " El" << e; } cout << endl; // Print number of solutions stored nSolutions = model.get(GRB_IntAttr_SolCount); cout << "Number of solutions found: " << nSolutions << endl; // Print objective values of solutions if (nSolutions > 10) nSolutions = 10; cout << "Objective values for first " << nSolutions; cout << " solutions:" << endl; for (i = 0; i < nSubsets; i++) { model.set(GRB_IntParam_ObjNumber, i); cout << "\tSet" << i; for (e = 0; e < nSolutions; e++) { cout << " "; model.set(GRB_IntParam_SolutionNumber, e); double val = model.get(GRB_DoubleAttr_ObjNVal); cout << std::setw(6) << val; } cout << endl; } } catch (GRBException e) { cout << "Error code = " << e.getErrorCode() << endl; cout << e.getMessage() << endl; } catch (...) { cout << "Exception during optimization" << endl; } // Free environment/vars delete[] Elem; delete env; return 0; }
