TY - JOUR
T1 - Multiobjective evolutionary algorithms
T2 - A survey of the state of the art
AU - Zhou, Aimin
AU - Qu, Bo Yang
AU - Li, Hui
AU - Zhao, Shi Zheng
AU - Suganthan, Ponnuthurai Nagaratnam
AU - Zhangd, Qingfu
PY - 2011/3
Y1 - 2011/3
N2 - A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.
AB - A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.
KW - Evolutionary multiobjective optimization
KW - Multicriteria decision making
KW - Multiobjective evolutionary algorithms
KW - Multiobjective optimization
UR - https://www.scopus.com/pages/publications/79960530761
U2 - 10.1016/j.swevo.2011.03.001
DO - 10.1016/j.swevo.2011.03.001
M3 - 文献综述
AN - SCOPUS:79960530761
SN - 2210-6502
VL - 1
SP - 32
EP - 49
JO - Swarm and Evolutionary Computation
JF - Swarm and Evolutionary Computation
IS - 1
ER -