自然启发与生物启发优化的综合分类法：灵感来源与算法行为、批判性分析及建议（2020年至2024年）
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)
AI总结：
针对生物启发优化算法数量激增、灵感与行为脱节的问题，本文提出两种分类法，对518种相关算法分类分析，发现超四分之一新算法是经典算法变体，为该领域研究提供分类参考与发展建议。
摘要：
近年来，模拟生物过程以解决复杂问题的生物启发优化方法在相关文献中日益流行，大量相关方案的涌现证明了学界对该领域的兴趣与日俱增。自然启发与生物启发算法、应用及指导准则的数量增长，也凸显了这一领域受关注程度的提升。然而，生物启发算法数量呈指数级增长的态势，给该研究领域的未来发展轨迹带来了挑战。在本文档的五个版本迭代过程中，相关方法的数量持续增长，且存在新的生物学描述被置于实际问题解决能力之上的问题。本文提出了两种综合分类法：一种基于生物相似性原理构建，另一种则基于与最初受生物启发的种群模型迭代相关的操作层面构建。因此，研究人员可借助这两种分类法，依据灵感来源和各算法表现出的行为这两项标准，将现有的算法发展成果归入明确定义的类别中。基于这些分类法，我们对518种遵循自然启发与生物启发原理的算法进行了分类。我们对这些类别中的每一种算法都开展了详尽研究，从而对设计趋势与相似性进行了批判性综合分析，并为每一种提出的方案找到了最为相似的经典算法。通过分析我们得出结论：算法的自然灵感来源与其行为之间往往关联较弱。此外，不同算法在行为层面的相似性，要高于其公开披露内容中所宣称的程度：具体而言，我们发现受评审的求解器中超过四分之一是经典算法的变体。对这些算法的分析结论带来了若干经验教训。
英文摘要：
In recent years, bio-inspired optimization methods, which mimic biological processes to solve complex problems, have gained popularity in recent literature. The proliferation of proposals prove the growing interest in this field. The increase in nature- and bio-inspired algorithms, applications, and guidelines highlights growing interest in this field. However, the exponential rise in the number of bio-inspired algorithms poses a challenge to the future trajectory of this research domain. Along the five versions of this document, the number of approaches grows incessantly, and where having a new biological description takes precedence over real problem-solving. This document presents two comprehensive taxonomies. One based on principles of biological similarity, and the other one based on operational aspects associated with the iteration of population models that initially have a biological inspiration. Therefore, these taxonomies enable researchers to categorize existing algorithmic developments into well-defined classes, considering two criteria: the source of inspiration, and the behavior exhibited by each algorithm. Using these taxonomies, we classify 518 algorithms based on nature-inspired and bio-inspired principles. Each algorithm within these categories is thoroughly examined, allowing for a critical synthesis of design trends and similarities, and identifying the most analogous classical algorithm for each proposal. From our analysis, we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-fourth of the reviewed solvers are versions of classical algorithms. The conclusions from the analysis of the algorithms lead to several learned lessons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
