The weighted-sum scalarization combines two objective functions into a single aggregate objective J by multiplying the first objective by a weight w1 and the second objective by the complementary weight 1−w1.
Conditions: There are exactly two objectives being combined.; The second weight is defined as 1−w1.; The objective is to minimize or maximize the combined scalar J.
The weighted-sum scalarization combines two objective functions into a single aggregate objective J by multiplying the first objective by a weight w1 and the second objective by the complementary weight 1−w1.
Conditions: There are exactly two objectives being combined.; The second weight is defined as 1−w1.; The objective is to minimize or maximize the combined scalar J.
The weight table serves as the computational scaffold for exploring the family of weighted problems. It lists candidate values for the weight w1, allowing the user to compute the aggregate objective J and record the resulting objective values OBJ1 and OBJ2 for each weight choice.
Conditions: Use a set of candidate weights w1.; Compute J for each weight.; Record the resulting solution’s OBJ1 and OBJ2 values.
The weight table serves as the computational scaffold for exploring the family of weighted problems. It lists candidate values for the weight w1, allowing the user to compute the aggregate objective J and record the resulting objective values OBJ1 and OBJ2 for each weight choice.
Conditions: Use a set of candidate weights w1.; Compute J for each weight.; Record the resulting solution’s OBJ1 and OBJ2 values.