Evolutionary optimization on continuous dynamic constrained problems : An analysis

Trung Thanh NGUYEN*, Xin YAO

*Corresponding author for this work

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5 Citations (Scopus)


Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is little research on whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs). This chapter investigates this issue. The chapter will present some studies on the characteristics that can make DCOPs difficult to solve by some existing dynamic optimization (DO) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed. © 2013 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationEvolutionary Computation for Dynamic Optimization Problems
EditorsShengxiang YANG, Xin YAO
Number of pages25
ISBN (Electronic)9783642384165
ISBN (Print)9783642384158, 9783642448430
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


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  • Evolutionary Computation for Dynamic Optimization Problems

    YANG, S. (ed.) & YAO, X. (ed.), 2013, Heidelberg: Springer. 470 p. (Studies in Computational Intelligence; vol. 490)

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