TY - JOUR
T1 - A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades
T2 - Part B
AU - Yazdani, Danial
AU - Cheng, Ran
AU - Yazdani, Donya
AU - Branke, Jurgen
AU - Jin, Yaochu
AU - Yao, Xin
N1 - Funding Information:
Manuscript received November 27, 2020; accepted February 1, 2021. Date of publication February 18, 2021; date of current version July 30, 2021. 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. (Corresponding author: Ran Cheng.) Danial Yazdani and Ran Cheng are with the Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China (e-mail: danial.yazdani@gmail.com; chengr@sustc.edu.cn).
Publisher Copyright:
© 1997-2012 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - 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.
AB - 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.
KW - Continuous dynamic real-world problems
KW - dynamic benchmark problems
KW - evolutionary algorithms
KW - future directions
KW - performance indicators
KW - unconstrained continuous dynamic optimization
UR - http://www.scopus.com/inward/record.url?scp=85101751260&partnerID=8YFLogxK
U2 - 10.1109/TEVC.2021.3060012
DO - 10.1109/TEVC.2021.3060012
M3 - Article
SN - 1089-778X
VL - 25
SP - 630
EP - 650
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
IS - 4
M1 - 9356720
ER -