Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Authors

Colleges, School and Institutes

External organisations

  • University of Exeter
  • Ankara University
  • Michigan State University

Abstract

Multi-objective optimization with more than three objectives has become one of the most active topics in evolutionary multi-objective optimization (EMO). However, most existing studies limit their experiments up to 15 or 20 objectives, although they claimed to be capable of handling as many objectives as possible. To broaden the insights in the behavior of EMO methods when facing a massively large number of objectives, this paper presents some preliminary empirical investigations on several established scalable benchmark problems with 25, 50, 75 and 100 objectives. In particular, this paper focuses on the behavior of the currently pervasive reference point based EMO methods, although other methods can also be used. The experimental results demonstrate that the reference point based EMO method can be viable for problems with a massively large number of objectives, given an appropriate choice of the distance measure. In addition, sufficient population diversity should be given on each weight vector or a local niche, in order to provide enough selection pressure. To the best of our knowledge, this is the first time an EMO methodology has been considered to solve a massively large number of conflicting objectives.

Details

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
Subtitle of host publication9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings
EditorsHeike Trautmann, Günter Rudolph, Kathrin Klamroth, Oliver Schütze , Margaret Wiecek, Yaochu Jin, Christian Grimme
Publication statusE-pub ahead of print - 19 Feb 2017
Event9th International Conference on Multi-Criterion Optimization, EMO 2017 - Munster, Germany
Duration: 19 Mar 201722 Mar 2017

Publication series

NameLecture Notes in Computer Science
Volume10173
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Multi-Criterion Optimization, EMO 2017
Country/TerritoryGermany
CityMunster
Period19/03/1722/03/17