Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

Danial YAZDANI, Mohammad Nabi OMIDVAR, Ran CHENG*, Jürgen BRANKE, Trung Thanh NGUYEN, Xin YAO

*Corresponding author for this work

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

14 Citations (Scopus)

Abstract

Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this article, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties, such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.

Original languageEnglish
Pages (from-to)3380-3393
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume52
Issue number5
DOIs
Publication statusPublished - May 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Dynamic environments
  • dynamic optimization problems (DOPs)
  • evolutionary dynamic optimization
  • moving peaks benchmark (MPB)
  • survey
  • tracking moving optimum (TMO)

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