Title :
Multiobjective genetic algorithms applied to solve optimization problems
Author :
Dias, Alexandre H F ; De Vasconcelos, Jõao A.
Author_Institution :
Acesita Co., Timoteo, Brazil
fDate :
3/1/2002 12:00:00 AM
Abstract :
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
Keywords :
electromagnetic field theory; genetic algorithms; sorting; NSGA; Pareto-optimum front mapping; TEAM benchmark problem; electromagnetic problems; evolutionary algorithms; multiobjective genetic algorithms; multiobjective optimization problems; nondominated sorting genetic algorithm; optimization problems; quench physical condition; Algorithm design and analysis; Electromagnetic analysis; Evolutionary computation; Genetic algorithms; Performance analysis; Performance evaluation; Sections; Sorting; Testing;
Journal_Title :
Magnetics, IEEE Transactions on
