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

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

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

46 Citations (Scopus)

Abstract

This article presents the second Part of a two-Part survey that reviews evolutionary dynamic optimization (EDO) for single-objective unconstrained continuous problems over the last two decades. While in the first part, we reviewed the components of dynamic optimization algorithms (DOAs); in this part, we present an in-depth review of the most commonly used benchmark problems, performance analysis methods, static optimization methods used in the framework of DOAs, and real-world applications. Compared to the previous works, this article provides a new taxonomy for the benchmark problems used in the field based on their baseline functions and dynamics. In addition, this survey classifies the commonly used performance indicators into fitness/error-based and efficiency-based ones. Different types of plots used in the literature for analyzing the performance and behavior of algorithms are also reviewed. Furthermore, the static optimization algorithms that are modified and utilized in the framework of DOAs as the optimization components are covered. We then comprehensively review some real-world dynamic problems that are optimized by EDO methods. Finally, some challenges and opportunities are pointed out for future directions. © 1997-2012 IEEE.
Original languageEnglish
Article number9356720
Pages (from-to)630-650
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

  • Continuous dynamic real-world problems
  • dynamic benchmark problems
  • evolutionary algorithms
  • future directions
  • performance indicators
  • unconstrained continuous dynamic optimization

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