Genetic algorithms optimization for normalized normal constraint method under Pareto construction

This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss–Newton optimization methods which are used by the original normalized normal constraint method.