A comparative study use of OTL for many-objective optimization

Jinhua Zheng, Hui Bai, Ruimin Shen, Miqing Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.

Original languageEnglish
Title of host publicationGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
EditorsSara Silva
PublisherAssociation for Computing Machinery
Pages1411-1412
Number of pages2
ISBN (Electronic)9781450334884
DOIs
Publication statusPublished - 11 Jul 2015
Event17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015

Publication series

NameGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Conference

Conference17th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/TerritorySpain
CityMadrid
Period11/07/1515/07/15

Bibliographical note

Funding Information:
The authors wish to thank the support of the National Natural Science Foundation of China (Grant Nos. 61379062, 61372049, 61403326), the Science Research Project of the Education Office of Hunan Province (Grant Nos. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant Nos. 14JJ2072, 13JJ8006), the Science and Technology Project of Hunan Province (Grant No. 2014GK3027), the Construct Program of the Key Discipline in Hunan Province, and the Hunan Provincial Innovation Foundation For Post- graduate (Grant No. CX2013A011).

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A comparative study use of OTL for many-objective optimization'. Together they form a unique fingerprint.

Cite this