一种改进共轭梯度法的混合蜻蜓算法

IF 2 Q3 TELECOMMUNICATIONS
Layth Riyadh Khaleel, Ban Ahmed Mitras Prof.
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引用次数: 1

摘要

蜻蜓算法(Dragonfly Algorithm, DA)是一种元启发式算法,是Mirjalili(2015)提出的一种模拟蜻蜓觅食和迁徙行为的新算法。本文通过推导新的共轭系数,提出了一种改进的共轭梯度算法。证明了该算法的充分下降性和全局收敛性。利用改进共轭梯度算法的特点,将随机生成的初级社会作为蜻蜓优化算法的初级社会,提出了一种新的蜻蜓混合算法(DA)。将混合算法应用于不同维数的高测量优化函数(10),测试了混合算法的效率,与原算法相比,混合算法的结果非常好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Hybrid Dragonfly Algorithm with Modified Conjugate Gradient Method
Dragonfly Algorithm (DA) is a meta-heuristic algorithm, It is a new algorithm proposed by Mirjalili in (2015) and it simulate the behavior of dragonflies in their search for food and migration. In this paper, a modified conjugate gradient algorithm is proposed by deriving new conjugate coefficient. The sufficient descent and the global convergence properties for the proposed algorithm are proved. Novel hybrid algorithm of the dragonfly (DA) was proposed with modified conjugate gradient Algorithm which develops the elementary society that is randomly generated as the primary society for the dragonfly optimization algorithm using the characteristics of the modified conjugate gradient algorithm. The efficiency of the hybrid algorithm was measured by applying it to (10) of the optimization functions of high measurement with different dimensions and the results of the hybrid algorithm were very good in comparison with the original algorithm.
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来源期刊
CiteScore
5.30
自引率
5.00%
发文量
18
审稿时长
15 weeks
期刊介绍: The Journal of Computer Networks and Communications publishes articles, both theoretical and practical, investigating computer networks and communications. Articles explore the architectures, protocols, and applications for networks across the full spectrum of sizes (LAN, PAN, MAN, WAN…) and uses (SAN, EPN, VPN…). Investigations related to topical areas of research are especially encouraged, including mobile and wireless networks, cloud and fog computing, the Internet of Things, and next generation technologies. Submission of original research, and focused review articles, is welcomed from both academic and commercial communities.
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