multiobj_cs.cs
/*版权2021,Gurobi优化，L狗万app足彩LC */ /*想覆盖三种不同的集合，但受允许使用的元素的共同预算。然而，这些设置有不同的优先考虑;我们用多目标优化来解决这个问题。* /使用系统;使用Gurobi;class multiobj_cs {static void Main() {try{//样本数据int groundSetSize = 20;int nSubsets = 4;int预算= 12;双组[,]= new双[,]{{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 = new int[] {3,2,2,1}; double[] SetObjWeight = new double[] {1.0, 0.25, 1.25, 1.0}; int e, i, status, nSolutions; // Create environment GRBEnv env = new GRBEnv("multiobj_cs.log"); // Create initial model GRBModel model = new GRBModel(env); model.ModelName = "multiobj_cs"; // Initialize decision variables for ground set: // x[e] == 1 if element e is chosen for the covering. GRBVar[] Elem = model.AddVars(groundSetSize, GRB.BINARY); for (e = 0; e < groundSetSize; e++) { string vname = string.Format("El{0}", e); Elem[e].VarName = vname; } // Constraint: limit total number of elements to be picked to be at most // Budget GRBLinExpr lhs = new GRBLinExpr(); for (e = 0; e < groundSetSize; e++) { lhs.AddTerm(1.0, Elem[e]); } model.AddConstr(lhs, GRB.LESS_EQUAL, Budget, "Budget"); // Set global sense for ALL objectives model.ModelSense = GRB.MAXIMIZE; // Limit how many solutions to collect model.Parameters.PoolSolutions = 100; // Set and configure i-th objective for (i = 0; i < nSubsets; i++) { string vname = string.Format("Set{0}", i); GRBLinExpr objn = new GRBLinExpr(); for (e = 0; e < groundSetSize; e++) { objn.AddTerm(Set[i,e], Elem[e]); } model.SetObjectiveN(objn, i, SetObjPriority[i], SetObjWeight[i], 1.0 + i, 0.01, vname); } // Save problem model.Write("multiobj_cs.lp"); // Optimize model.Optimize(); // Status checking status = model.Status; if (status == GRB.Status.INF_OR_UNBD || status == GRB.Status.INFEASIBLE || status == GRB.Status.UNBOUNDED ) { Console.WriteLine("The model cannot be solved " + "because it is infeasible or unbounded"); return; } if (status != GRB.Status.OPTIMAL) { Console.WriteLine("Optimization was stopped with status {0}", status); return; } // Print best selected set Console.WriteLine("Selected elements in best solution:"); Console.Write("\t"); for (e = 0; e < groundSetSize; e++) { if (Elem[e].X < .9) continue; Console.Write("El{0} ", e); } Console.WriteLine(); // Print number of solutions stored nSolutions = model.SolCount; Console.WriteLine("Number of solutions found: {0}", nSolutions); // Print objective values of solutions if (nSolutions > 10) nSolutions = 10; Console.WriteLine("Objective values for first {0} solutions:", nSolutions); for (i = 0; i < nSubsets; i++) { model.Parameters.ObjNumber = i; Console.Write("\tSet" + i); for (e = 0; e < nSolutions; e++) { model.Parameters.SolutionNumber = e; Console.Write("{0,8}", model.ObjNVal); } Console.WriteLine(); } model.Dispose(); env.Dispose(); } catch (GRBException e) { Console.WriteLine("Error code = {0}", e); Console.WriteLine(e.Message); } } }
