A Survey of Evolutionary Continuous Dynamic Optimization over Two Decades-Part A

Danial YAZDANI, Ran CHENG, Donya YAZDANI, Jurgen BRANKE, Yaochu JIN, Xin YAO

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

43 Citations (Scopus)

Abstract

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part article, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms (DOAs), namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multipopulation methods, which are underrepresented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an in-depth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the DOAs, and dynamic real-world applications. Finally, several opportunities for future work are pointed out. © 1997-2012 IEEE.
Original languageEnglish
Article number9356715
Pages (from-to)609-629
Number of pages21
JournalIEEE Transactions on Evolutionary Computation
Volume25
Issue number4
Early online date18 Feb 2021
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531; in part by the Guangdong Provincial Key Laboratory under Grant 2020B121201001; in part by the National Natural Science Foundation of China under Grant 61903178, Grant 61906081, and Grant U20A20306; in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386; and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.

Keywords

  • Change detection
  • evolutionary algorithms (EA)
  • multipopulation
  • response component
  • taxonomy
  • unconstrained continuous dynamic optimization

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