Evolutionary optimization on continuous dynamic constrained problems : An analysis

Trung Thanh NGUYEN; Xin YAO

doi:10.1007/978-3-642-38416-5_8

Evolutionary optimization on continuous dynamic constrained problems : An analysis

Trung Thanh NGUYEN^*, Xin YAO

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

5 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Evolutionary Computation for Dynamic Optimization Problems
Editors	Shengxiang YANG, Xin YAO
Publisher	Springer
Chapter	8
Pages	193-217
Number of pages	25
ISBN (Electronic)	9783642384165
ISBN (Print)	9783642384158, 9783642448430
DOIs	https://doi.org/10.1007/978-3-642-38416-5_8
Publication status	Published - 2013
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
Volume	490
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Access to Document

10.1007/978-3-642-38416-5_8

Cite this

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title = "Evolutionary optimization on continuous dynamic constrained problems : An analysis",

abstract = "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. {\textcopyright} 2013 Springer-Verlag Berlin Heidelberg.",

author = "NGUYEN, {Trung Thanh} and Xin YAO",

year = "2013",

doi = "10.1007/978-3-642-38416-5_8",

language = "English",

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Evolutionary optimization on continuous dynamic constrained problems : An analysis. / NGUYEN, Trung Thanh; YAO, Xin.
Evolutionary Computation for Dynamic Optimization Problems. ed. / Shengxiang YANG; Xin YAO. Springer, 2013. p. 193-217 (Studies in Computational Intelligence; Vol. 490).

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

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AU - YAO, Xin

PY - 2013

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AB - 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.

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Evolutionary optimization on continuous dynamic constrained problems : An analysis

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