The hypervolume indicator as a performance measure in dynamic optimization

Sabrina Oliveira*, Elizabeth F. Wanner, Sérgio R. de Souza, Leonardo C.T. Bezerra, Thomas Stützle

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


In many real world problems the quality of solutions needs to be evaluated at least according to a bi-objective non-dominated front, where the goal is to optimize solution quality using as little computational resources as possible. This is even more important in the context of dynamic optimization, where quickly addressing problem changes is critical. In this work, we relate approaches for the performance assessment of dynamic optimization algorithms to the existing literature on bi-objective optimization. In particular, we introduce and investigate the use of the hypervolume indicator to compare the performance of algorithms applied to dynamic optimization problems. As a case study, we compare variants of a state-of-the-art dynamic ant colony algorithm on the traveling salesman problem with dynamic demands (DDTSP). Results demonstrate that our proposed approach accurately measures the desirable characteristics one expects from a dynamic optimizer and provides more insights than existing alternatives.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
EditorsSanaz Mostaghim, Kaisa Miettinen, Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Patrick Reed
Number of pages13
ISBN (Print)9783030125974
Publication statusPublished - 3 Feb 2019
Event10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 - East Lansing, United States
Duration: 10 Mar 201913 Mar 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019
Country/TerritoryUnited States
CityEast Lansing


  • Dynamic optimization
  • Multi-objective optimization
  • Performance assessment


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