Improvement of reference points for decomposition based multi-objective evolutionary algorithms

Hemant Kumar SINGH*, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)


A multi-objective optimization problem (MOP) involves simultaneous minimization or maximization of more than one conflicting objectives. Such problems are commonly encountered in a number of domains, such as engineering, finance, operations research, etc. In the recent years, algorithms based on decomposition have shown commendable success in solving MOPs. In particular they have been helpful in overcoming the limitation of Pareto-dominance based ranking when the number of objectives is large. Decomposition based evolutionary algorithms divide an MOP into a number of simpler sub-problems and solve them simultaneously in a cooperative manner. In order to define the sub-problems, a reference point is needed to construct reference vectors in the objective space to guide the corresponding sub-populations. However, the effect of the choice of this reference point has been scarcely studied in literature. Most of the existing works simply construct the reference point using the minimum objective values in the current nondominated population. Some of the recent studies have gone beyond and suggested the use of optimistic, pessimistic or dynamic reference point specification. In this study, we first qualitatively examine the implications of using different strategies to construct the reference points. Thereafter, we suggest an alternative method which relies on identifying promising reference points rather than specifying them. In the proposed approach, each objective is individually minimized in order to estimate a point close to the true ideal point to identify such reference points. Some initial results and analysis are presented to demonstrate the potential benefits and limitations of the approach. Overall, the approach demonstrates promising results but needs further development for achieving more significant improvements in solving MOPs. © Springer International Publishing AG 2017.
Original languageEnglish
Title of host publicationSimulated Evolution and Learning : 11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings
EditorsYuhui SHI, Kay Chen TAN, Mengjie ZHANG, Ke TANG, Xiaodong LI, Qingfu ZHANG, Ying TAN, Martin MIDDENDORF, Yaochu JIN
Number of pages13
ISBN (Electronic)9783319687599
ISBN (Print)9783319687582
Publication statusPublished - 2017
Externally publishedYes
Event11th International Conference on Simulated Evolution and Learning, SEAL 2017 - Shenzhen, China
Duration: 10 Nov 201713 Nov 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


Conference11th International Conference on Simulated Evolution and Learning, SEAL 2017
Abbreviated titleSEAL 2017

Bibliographical note

The first author would like to acknowledge the Australian Bicentennial Fellowship from the Menzies Centre, Kings College London, which supported his research visit to the University of Birmingham for this work, where the second author holds a concurrent position. The work was also partially supported by Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284) and NSFC (Grant No. 61329302).


  • Multi-objective optimization
  • Reference point
  • Reference vector


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