一种改进的Jaya优化算法

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mahamed G. H. Omran, Giovanni Iacca
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引用次数: 0

摘要

提出了一种改进的Jaya优化算法Jaya2,以牺牲原有的Jaya优化算法设计为代价,提高Jaya优化算法的性能。该方法将解排列在环形拓扑结构中,以减少过早收敛的可能性。此外,在优化过程中,采用种群大小缩减法自动调整种群大小。此外,讨论了原始Jaya的翻译依赖问题,并提出了一种替代的解决方案更新操作。为了测试Jaya2,我们将其与九种不同的优化方法在CEC 2020基准函数和CEC 2011实际优化问题上进行了比较。结果表明,Jaya2在测试问题上具有很强的竞争力,通常优于大多数方法。拥有一种易于实现且参数调整较少的方法是非常可取的,因为具有基本编程技能的不同学科的研究人员可以使用它来解决他们的优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved Jaya optimization algorithm with ring topology and population size reduction
Abstract An improved variant of the Jaya optimization algorithm, called Jaya2, is proposed to enhance the performance of the original Jaya sacrificing its algorithmic design. The proposed approach arranges the solutions in a ring topology to reduce the likelihood of premature convergence. In addition, the population size reduction is used to automatically adjust the population size during the optimization process. Moreover, the translation dependency problem of the original Jaya is discussed, and an alternative solution update operation is proposed. To test Jaya2, we compare it with nine different optimization methods on the CEC 2020 benchmark functions and the CEC 2011 real-world optimization problems. The results show that Jaya2 is highly competitive on the tested problems where it generally outperforms most approaches. Having an easy-to-implement approach with little parameter tuning is highly desirable since researchers from different disciplines with basic programming skills can use it to solve their optimization problems.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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