Qawqzeh Yousef, Alharbi Mafawez T, Jaradat Ayman, Abdul Sattar Khalid Nazim
Department of Computer Science and Engineering, Hafr Al Batin University, Hafr AL Batin, Saudi Arabia.
Department of Natural and Applied Sciences, Buraydah Community College, Qassim University, Buraydeh, Qassim, Saudi Arabia.
PeerJ Comput Sci. 2021 Aug 25;7:e696. doi: 10.7717/peerj-cs.696. eCollection 2021.
This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively.
SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015-2021) that belongs to SI algorithms are reviewed and summarized.
It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm.
The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.
本综述着重回顾群体智能算法（粒子群优化算法（PSO）、蚁群优化算法（ACO）、人工蜂群算法（ABC）和萤火虫算法（FA））在调度与优化问题方面的近期出版物。群体智能（SI）可被描述为自然生物（动物、鱼类和昆虫）的智能行为。实际上，它基于智能体群体，其中这些智能体之间以及与它们的环境有着可靠的联系。在这样的群体中，每个智能体（成员）依据特定规则行事，这些规则使其能够最大化该特定群体的整体效用。它可被描述为特定群体中自组织成员之间的集体智慧。事实上，生物学启发了许多研究人员模仿某些自然群体（鸟类、动物或昆虫）的行为来有效解决一些计算问题。
在云计算环境中利用群体智能技术来寻求最优调度策略。因此，对属于群体智能算法的近期出版物（2015 - 2021年）进行了综述和总结。
显然，用于云计算优化的算法数量正在迅速增加。与PSO、ACO、ABC和FA相关的期刊论文数量显著增加。然而，值得注意的是，许多最近出现的算法是基于对原始群体智能算法尤其是PSO算法的改进而产生的。
这项工作的主要目的是激励感兴趣的研究人员开发和创新基于群体智能的新解决方案，以处理复杂的多目标计算问题。
