An empirical comparison of several recent multi-objective evolutionary algorithms

Thomas White, Shan He*

*Corresponding author for this work

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

1 Citation (Scopus)


Many real-world problems can be formulated as multi-objective optimisation problems, in which many potentially conflicting objectives need to be optimized simultaneously. Multi-objective optimisation algorithms based on Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs) have been proven to be superior to other traditional algorithms such as goal programming. In the past years, several novel Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed. Rather than based on traditional GAs, these algorithms extended other EAs including novel EAs such as Scatter Search and Particle Swarm Optimiser to handle multi-objective problems. However, to the best of our knowledge, there is no fair and systematic comparison of these novel MOEAs. This paper, for the first time, presents the results of an exhaustive performance comparison of an assortment of 5 new and popular algorithms on the DTLZ benchmark functions using a set of well-known performance measures. We also propose a novel performance measure called unique hypervolume, which measures the volume of objective space dominated only by one or more solutions, with respect to a set of solutions. Based on our results, we obtain some important observations on how to choose an appropriate MOA according to the preferences of the user.

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations - 8th IFIP WG 12.5 International Conference, AIAI 2012, Proceedings
Number of pages10
EditionPART 1
Publication statusPublished - 2012
Event8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012 - Halkidiki, Greece
Duration: 27 Sept 201230 Sept 2012

Publication series

NameIFIP Advances in Information and Communication Technology
NumberPART 1
Volume381 AICT
ISSN (Print)1868-4238


Conference8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012

Bibliographical note

Copyright 2013 Elsevier B.V., All rights reserved.


  • Comparison
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Multi-objective optimisation

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management


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