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.

中文翻译：

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过去两年的进化连续动态优化综述——A 部分

许多现实世界的优化问题都是动态的。动态优化领域处理搜索空间随时间变化的问题。在这篇由两部分组成的文章中，我们对过去二十年中单目标无约束连续问题的进化动态优化研究进行了全面调查。在本次调查的 A 部分，我们为动态优化算法 (DOA) 的组成部分提出了一种新的分类法，即收敛检测、变化检测、显式归档、多样性控制以及种群划分和管理。与现有分类法相比，提议的分类法涵盖了一些额外的重要组成部分，例如收敛检测和计算资源分配。而且，我们显着扩展和改进了多样性控制和多种群方法的分类，这些在现有分类法中代表性不足。然后，我们根据建议的分类法提供不同组件的详细技术描述和分析。本调查的 B 部分对最常用的基准问题、性能分析方法、用作 DOA 中优化组件的静态优化算法以及动态实际应用程序进行了深入分析。最后，指出了未来工作的几个机会。本调查的 B 部分对最常用的基准问题、性能分析方法、用作 DOA 中优化组件的静态优化算法以及动态实际应用程序进行了深入分析。最后，指出了未来工作的几个机会。本调查的 B 部分对最常用的基准问题、性能分析方法、用作 DOA 中优化组件的静态优化算法以及动态实际应用程序进行了深入分析。最后，指出了未来工作的几个机会。