Multiobjective genetic algorithms applied to solve optimization problems

In this paper, we discuss multiobjective optimization problems solved by evolutionary algorithms. We present the nondominated sorting genetic algorithm (NSGA) to solve this class of problems and its performance is analyzed by comparing its results with those obtained with four other algorithms. Finally, the NSGA is applied to solve the TEAM benchmark problem 22 without considering the quench physical condition to map the Pareto-optimum front. The results in both analytical and electromagnetic problems show its effectiveness.