A Computational Algorithm Based on Normalization for Constructing the Pareto Front of Multiobjective Optimization Problems

In this paper, we have presented a computational algorithm based on normalization for constructing the Pareto front (PF) in multi-objective optimization problems (MOP). Some modifications of the epsilon-constraint method were suggested by Ehrgott et al., by including slack and surplus variables in the formulation of this method. In this paper, we replaced the unknown value of epsilon by a controllable step length such that the feasible set of the problem will be non-empty. The proposed algorithm is applied to some illustrative examples to demonstrate its computational efficiency.

Keywords:

multi-objective , optimization problem , Pareto front , scalarization techniques , computationally efficient algorithm.