云计算中用于增强负载平衡的受自然启发的优化算法：分类、比较分析和未来趋势的全面回顾

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-06-28 DOI:10.1016/j.swevo.2025.102053

Farida Siddiqi Prity

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引用次数: 0

摘要

云计算确保了可伸缩的按需资源供应，但高效的负载平衡仍然是一个挑战。传统方法在动态工作负载下常常失败，这促使人们对自然启发的优化算法（nioa）产生了兴趣。本文研究了应用于云负载平衡的47个nioa，涵盖了它们的原理、适应性和性能。在一项长达十年的文献调查（2014-2024）的支持下，一种新的分类法从十个维度对这些算法进行了分类。比较分析和基于模拟的案例研究突出了它们的优势、局限性和适用性。图表、图形和表格用于清晰地可视化和比较结果。该研究指出了研究差距并提出了建议，强调了nioa在提高云性能方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Nature-Inspired optimization algorithms for enhanced load balancing in cloud computing: A comprehensive review with taxonomy, comparative analysis, and future trends

Cloud computing ensures scalable, on-demand resource provisioning, yet efficient load balancing remains a challenge. Traditional methods often fail under dynamic workloads, prompting interest in nature-inspired optimization algorithms (NIOAs). This review examines 47 NIOAs applied to cloud load balancing, covering their principles, adaptations, and performance. A novel taxonomy classifies these algorithms across ten dimensions, supported by a decade-long literature survey (2014–2024). Comparative analyses and a simulation-based case study highlight their strengths, limitations, and applicability. Charts, graphs, and tables are used to clearly visualize and compare the results. The study identifies research gaps and offers recommendations, underscoring NIOAs’ potential for enhancing cloud performance.

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.