Dynamic Multiobjectives Optimization with a Changing Number of Objectives

Renzhi CHEN, Ke LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

116 Citations (Scopus)


Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature. Instead of changing the shape or position of the Pareto-optimal front/set (PF/PS) when having time-dependent objective functions, increasing or decreasing the number of objectives usually leads to the expansion or contraction of the dimension of the PF/PS manifold. Unfortunately, most existing dynamic handling techniques can hardly be adapted to this type of dynamics. In this paper, we report our attempt toward tackling the DMO problems with a changing number of objectives. We implement a dynamic two-archive evolutionary algorithm which maintains two co-evolving populations simultaneously. In particular, these two populations are complementary to each other: one concerns more about the convergence while the other concerns more about the diversity. The compositions of these two populations are adaptively reconstructed once the environment changes. In addition, these two populations interact with each other via a mating selection mechanism. Comprehensive experiments are conducted on various benchmark problems with a time-dependent number of objectives. Empirical results fully demonstrate the effectiveness of our proposed algorithm. © 1997-2012 IEEE.
Original languageEnglish
Article number7886303
Pages (from-to)157-171
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Issue number1
Early online date25 Mar 2017
Publication statusPublished - Feb 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the EPSRC under Grant EP/K001523/1, and in part by the NSFC under Grant 61329302. The work of X. Yao was supported by the Royal Society Wolfson Research Merit Award.


  • Changing objectives
  • decomposition-based method
  • dynamic optimization
  • evolutionary algorithm (EA)
  • multiobjective optimization


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