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.

中文翻译：

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两个十年的进化连续动态优化综述——B 部分

本文介绍了一项由两部分组成的调查的第二部分，该调查回顾了过去二十年中单目标无约束连续问题的进化动态优化 (EDO)。在第一部分中，我们回顾了动态优化算法 (DOA) 的组成部分；在这一部分，我们深入回顾了最常用的基准测试问题、性能分析方法、DOA 框架中使用的静态优化方法以及实际应用。与之前的工作相比，本文基于基准函数和动力学为该领域使用的基准问题提供了一种新的分类法。此外，本次调查将常用的绩效指标分为基于适应度/误差和基于效率的指标。还回顾了文献中用于分析算法性能和行为的不同类型的图。此外，还涵盖了在 DOA 框架中作为优化组件修改和使用的静态优化算法。然后，我们全面回顾了一些通过 EDO 方法优化的现实世界动态问题。最后，指出了未来发展方向的一些挑战和机遇。