TY - GEN
T1 - Performance scaling of multi-objective evolutionary algorithms
AU - KHARE, V.
AU - YAO, X.
AU - DEB, K.
PY - 2003
Y1 - 2003
N2 - MOEAs are getting immense popularity in the recent past, mainly because of their ability to find a wide spread of Pareto-optimal solutions in a single simulation run. Various evolutionary approaches to multi-objective optimization have been proposed since 1985. Some of fairly recent ones are NSGA-II, SPEA2, PESA (which are included in this study) and others. They all have been mainly applied to two to three objectives. In order to establish their superiority over classical methods and demonstrate their abilities for convergence and maintenance of diversity, they need to be tested on higher number of objectives. In this study, these staterof-the-art MOEAs have been investigated for their scalability with respect to the number of objectives (2 to 8). They have also been compared on the basis of -(1) Their ability to converge to Pareto front, (2) Diversity of obtained non-dominated solutions and (3) Their running time. Four scalable test problems (DTLZ1, 2, 3 and 6) are used for the comparative study. © Springer-Verlag Berlin Heidelberg 2003.
AB - MOEAs are getting immense popularity in the recent past, mainly because of their ability to find a wide spread of Pareto-optimal solutions in a single simulation run. Various evolutionary approaches to multi-objective optimization have been proposed since 1985. Some of fairly recent ones are NSGA-II, SPEA2, PESA (which are included in this study) and others. They all have been mainly applied to two to three objectives. In order to establish their superiority over classical methods and demonstrate their abilities for convergence and maintenance of diversity, they need to be tested on higher number of objectives. In this study, these staterof-the-art MOEAs have been investigated for their scalability with respect to the number of objectives (2 to 8). They have also been compared on the basis of -(1) Their ability to converge to Pareto front, (2) Diversity of obtained non-dominated solutions and (3) Their running time. Four scalable test problems (DTLZ1, 2, 3 and 6) are used for the comparative study. © Springer-Verlag Berlin Heidelberg 2003.
UR - http://www.scopus.com/inward/record.url?scp=35248887077&partnerID=8YFLogxK
U2 - 10.1007/3-540-36970-8_27
DO - 10.1007/3-540-36970-8_27
M3 - Conference paper (refereed)
SN - 9783540018698
T3 - Lecture Notes in Computer Science
SP - 376
EP - 390
BT - Evolutionary Multi-Criterion Optimization : Second International Conference, EMO 2003, Faro, Portugal, April 8-11, 2003, Proceedings
A2 - FONSECA, Carlos M.
A2 - FLEMING, Peter J.
A2 - ZITZLER, Eckart
A2 - THIELE, Lothar
A2 - DEB, Kalyanmoy
PB - Springer Berlin Heidelberg
T2 - 2nd International Conference on Evolutionary Multi-Criterion Optimization, EMO 2003
Y2 - 8 April 2003 through 11 April 2003
ER -