TY - CHAP
T1 - Comparison between MOEA/D and NSGA-II on the multi-objective travelling salesman problem
AU - Peng, Wei
AU - Zhang, Qingfu
AU - Li, Hui
PY - 2009
Y1 - 2009
N2 - Most multiobjective evolutionary algorithms are based on Pareto dominance for measuring the quality of solutions during their search, among them NSGA-II is well-known. A very few algorithms are based on decomposition and implicitly or explicitly try to optimize aggregations of the objectives. MOEA/D is a very recent such an algorithm. One of the major advantages of MOEA/D is that it is very easy to design local search operator within it using well-developed single-objective optimization algorithms. This chapter compares the performance of MOEA/D and NSGA-II on the multiobjective travelling salesman problem and studies the effect of local search on the performance of MOEA/D. © 2009 Springer-Verlag Berlin Heidelberg.
AB - Most multiobjective evolutionary algorithms are based on Pareto dominance for measuring the quality of solutions during their search, among them NSGA-II is well-known. A very few algorithms are based on decomposition and implicitly or explicitly try to optimize aggregations of the objectives. MOEA/D is a very recent such an algorithm. One of the major advantages of MOEA/D is that it is very easy to design local search operator within it using well-developed single-objective optimization algorithms. This chapter compares the performance of MOEA/D and NSGA-II on the multiobjective travelling salesman problem and studies the effect of local search on the performance of MOEA/D. © 2009 Springer-Verlag Berlin Heidelberg.
UR - https://www.scopus.com/pages/publications/58149236790
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-58149236790&origin=recordpage
U2 - 10.1007/978-3-540-88051-6_14
DO - 10.1007/978-3-540-88051-6_14
M3 - RGC 12 - Chapter in an edited book (Author)
SN - 9783540880509
VL - 171
T3 - Studies in Computational Intelligence
SP - 309
EP - 324
BT - Multi-Objective Memetic Algorithms
PB - Springer
ER -