Suppr超能文献

一种结合Nelder Mead方法的布谷鸟搜索混合算法用于求解全局优化问题。

A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.

作者信息

Ali Ahmed F, Tawhid Mohamed A

机构信息

Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt ; Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, 900 McGill Road, Kamloop, BC V2C 0C8 Canada.

Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, 900 McGill Road, Kamloop, BC V2C 0C8 Canada ; Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Moharam Bey Alexandria, 21511 Egypt.

出版信息

Springerplus. 2016 Apr 18;5:473. doi: 10.1186/s40064-016-2064-1. eCollection 2016.

Abstract

Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.

摘要

布谷鸟搜索算法是一种很有前景的基于群体的元启发式方法。它已被应用于解决许多实际生活问题。在本文中,为了解决整数和极小极大优化问题,我们通过将布谷鸟搜索算法与Nelder-Mead方法相结合,提出了一种新的布谷鸟搜索算法。我们将所提出的算法称为混合布谷鸟搜索与Nelder-Mead方法(HCSNM)。HCSNM首先通过应用标准布谷鸟搜索进行若干次迭代来开始搜索,然后将获得的最佳解作为强化过程传递给Nelder-Mead算法,以加速搜索并克服标准布谷鸟搜索算法收敛速度慢的问题。所提出的算法在布谷鸟搜索算法的全局探索和Nelder-Mead方法的深度开发之间取得平衡。我们在七个整数规划问题和十个极小极大问题上测试了HCSNM算法,并与八种用于解决整数规划问题的算法和七种用于解决极小极大问题的算法进行了比较。实验结果表明了所提出算法的有效性及其在合理时间内解决整数和极小极大优化问题的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55d1/4835425/b2b43e591401/40064_2016_2064_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验