Table 1: Multiple objective optimization algorithms comparison.

Optimization Method



Weighted Sum Method

· Simple

· Difficult to set the weight factors

· Not applicable to non-convex problems

ε-Constraint Method

· Applicable to both convex and non-convex problems

· The ε vector has to be chosen carefully

Weighted Metric Method

· Guarantees finding all Pareto-optimal solutions with ideal solution

· Requires min and max objective values

· Requires that ideal solution can be found by independently optimizing each objective function


· Non-penalty constraint handling

· Fast and efficient convergence

· Search in a wide range and handle problems that start with non-feasible solutions

· Complexity


· Once a solution on Pareto-optimal front is found, it is stored in the external population

· Advantages from NSGA-II

· Requires a balance between the regular population size and the external population size


· Simple

· Worse than GAs in finding pareto solutions


· Fast

· Worse than GAs in finding pareto solutions