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

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.

Automated summary

This article reviews evolutionary dynamic optimization (EDO) for single-objective unconstrained continuous problems over the last two decades, with a focus on benchmark problems, performance analysis methods, static optimization methods, and real-world applications. The article provides a new taxonomy for benchmark problems and classifies performance indicators into fitness/error-based and efficiency-based categories. Additionally, it covers static optimization algorithms utilized in the framework of DOAs and reviews real-world dynamic problems optimized by EDO methods.