Convergence versus diversity in multiobjective optimization

Shouyong Jiang*, Shengxiang Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

14 Citations (Scopus)


Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergencefirst- and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting based selection method. While convergence-first sorting has continuously shown effectiveness for handling a variety of problems, it faces challenges to maintain well population diversity due to the overemphasis of convergence. In this paper, we propose a general diversity-first sorting method for multiobjective optimization. Based on the method, a new MOEA, called DBEA, is then introduced. DBEA is compared with the recently-developed nondominated sorting genetic algorithm III (NSGA-III) on different problems. Experimental studies show that the diversity-first method has great potential for diversity maintenance and is very competitive for many-objective optimization.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings
EditorsEmma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319458229
Publication statusPublished - 2016
Event14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom
Duration: 17 Sept 201621 Sept 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9921 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Parallel Problem Solving from Nature, PPSN 2016
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
This work was funded by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1.

Publisher Copyright:
© Springer International Publishing AG 2016.


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