Study The Effect of High Dimensional Objective Functions on Multi-Objective Evolutionary Algorithms

Multi-objective Evolutionary Algorithms (MOEAs) have been widely studied by many researchers and they have been used to solve different real-world applications that have more than one objective function. However, most MOEAs work well only when the number of objective functions is small such as two or three. The performance of MOEAs starts degrading significantly when number of objective functions increases. Therefore, there is increasing importance for studying and analyzing the effect of increasing the number of objective functions on the performance of current multi objective evolutionary algorithms. In this paper, the performance of three state-of-the-art multi objective evolutionary algorithms is investigated when increasing the number of objective functions. The tested algorithms are analyzed using test DTLZ test suit. The results show that SMPSO and NSGA-II algorithms are the best two algorithms for high number of objective functions. In addition, the results show that the running time of SMPSO and GDE3 algorithms was effected and increased much when the number of objective functions is large.