A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation

Many-objective optimization problems abbreviated as MaOPs with more than three objectives have attracted increasing interests due to their widely existing in a variety of real-world applications. This paper presents a novel many-objective population extremal optimization called MaOPEO-HM algorithm f...

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Vydáno v:Information sciences Ročník 498; s. 62 - 90
Hlavní autoři: Chen, Min-Rong, Zeng, Guo-Qiang, Lu, Kang-Di
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.09.2019
Témata:
Adaptive hybrid mutation operation
Many-objective evolutionary algorithms
Many-objective optimization problems
Many-objective population extremal optimization
NSGA-II
EFR-RR
HIMO
FFE
GrEA
RVEA
MOEA/DD
HV
PLM
MaOPEO-MNUM
MaOPs
SDE
AnD
PBI
MOEAs
WFG
MOEA/D-DE
EA
MNUM
MaOPEO
Adaptive hybrid mutation operation
MaOPEO-PLM
Many-objective evolutionary algorithms
IGD
NUM
NSGA-III
EO
MOEA/D-DU
ZDT
MOEA/D
RCGA
SPEA2
Two_Arch2
PD
APD
PF
θ-DEA
MOCell
MOPEO
MaOPEO-HM
MOEA/D-DRA
PEO
RM
Many-objective optimization problems
MaOEAs
PM
Many-objective population extremal optimization
DTLZ
ISSN:0020-0255, 1872-6291
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Shrnutí:Many-objective optimization problems abbreviated as MaOPs with more than three objectives have attracted increasing interests due to their widely existing in a variety of real-world applications. This paper presents a novel many-objective population extremal optimization called MaOPEO-HM algorithm for MaOPs by introducing a reference set based many-objective optimization mechanism into a recently developed population extremal optimization framework and designing an adaptive hybrid mutation operation for updating the population. Despite of the successful applications of extremal optimization in different kinds of numerical and engineering optimization problems, it has never been explored to the many-objective optimization domain so far. Because most of the existing many-objective evolutionary algorithms are usually guided by a single mutation operation, which has insufficient ability to exploit the search space of MaOPs and will get stuck at any local efficient front, it is the first attempt to design a novel hybrid mutation scheme in MaOPEO-HM algorithm by combining the advantages of polynomial mutation operator and multi-non-uniform mutation operator effectively. The experiment results for DTLZ test problems with 3, 5, 8, 10, and 15 objectives and WFG test problems with 3, 5, and 8 objectives have demonstrated the superiority of the proposed MaOPEO-HM to five state-of-the-art decomposition-based many-objective evolutionary algorithms including NSGA-III, RVEA, EFR-RR, θ-DEA, and MOEA/DD and two non-decomposition-based algorithms including GrEA and Two_Arch2. Furthermore, the great ability of the designed adaptive hybrid mutation operation incorporated into many-objective population extremal optimization (MaOPEO) has also been illustrated by comparing MaOPEO-HM and two MaOPEO algorithms only based on traditional multi-non-uniform mutation or polynomial mutation for DTLZ problems.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2019.05.048