TY - GEN
T1 - A multiobjective differential evolution based on decomposition for multiobjective optimization with variable linkages
AU - Li, Hui
AU - Zhang, Qingfu
PY - 2006
Y1 - 2006
N2 - Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblems by a conventional decomposition method. The optimal solution to each of these problems is a Pareto optimal solution to the MOP under consideration. An appropriate decomposition could make these individual Pareto solutions evenly distribute along the Pareto optimal front. MOEA/D aims at solving these scalar optimization subproblems simultaneously. In this paper, we propose, under the framework of MOEA/D, a multiobjective differential evolution based decomposition (MODE/D) for tackling variable linkages. Our experimental results show that MODE/D outperforms several other MOEAs on several test problems with variable linkages. © Springer-Verlag Berlin Heidelberg 2006.
AB - Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblems by a conventional decomposition method. The optimal solution to each of these problems is a Pareto optimal solution to the MOP under consideration. An appropriate decomposition could make these individual Pareto solutions evenly distribute along the Pareto optimal front. MOEA/D aims at solving these scalar optimization subproblems simultaneously. In this paper, we propose, under the framework of MOEA/D, a multiobjective differential evolution based decomposition (MODE/D) for tackling variable linkages. Our experimental results show that MODE/D outperforms several other MOEAs on several test problems with variable linkages. © Springer-Verlag Berlin Heidelberg 2006.
UR - https://www.scopus.com/pages/publications/33750268311
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33750268311&origin=recordpage
U2 - 10.1007/11844297_59
DO - 10.1007/11844297_59
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783540389903
T3 - Lecture Notes in Computer Science
SP - 583
EP - 592
BT - Parallel Problem Solving from Nature - PPSN IX
A2 - Runarsson, Thomas Philip
A2 - Beyer, Hans-Georg
A2 - Burke, Edmund
PB - Springer
CY - Berlin, Heidelberg
T2 - 9th International Conference on Parallel Problem Solving from Nature (PPSN IX)
Y2 - 9 September 2006 through 13 September 2006
ER -