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基于改进蛾火优化算法的多核支持向量回归在软件工作量估计中的应用

Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation.

作者信息

Li Jing, Sun Shengxiang, Xie Li, Zhu Chen, He Dubo

机构信息

Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan, 430033, China.

出版信息

Sci Rep. 2024 Jul 23;14(1):16892. doi: 10.1038/s41598-024-67197-1.

DOI:10.1038/s41598-024-67197-1
PMID:39043713
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11266436/
Abstract

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO .

摘要

本文提出了一种新颖的蛾火优化（MFO）算法，即通过多种改进策略增强的MFO算法（MISMFO），用于解决多核支持向量回归器（MKSVR）中的参数优化问题，并进一步采用MISMFO-MKSVR模型来处理软件工作量估计问题。在MISMFO中，应用逻辑混沌映射来增加初始种群多样性，同时实施变异和火焰数逐步减少机制以提高搜索效率，还使用自适应权重调整机制来加速收敛并平衡探索和利用。MISMFO模型在十五个基准函数和CEC 2020测试集上进行了验证。结果表明，MISMFO在收敛速度和准确性方面优于其他元启发式算法和MFO变体。此外，通过在五个软件工作量数据集上进行仿真测试了MISMFO-MKSVR模型，结果表明所提出的模型在软件工作量估计问题上具有更好的性能。MISMFO的Matlab代码可在https://github.com/loadstar1997/MISMFO上找到。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/43a91b6e917b/41598_2024_67197_Fig15_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/7eb0b25d292d/41598_2024_67197_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/f72396b7aa58/41598_2024_67197_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/8d33b4bb0ac2/41598_2024_67197_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/efc9d53d9016/41598_2024_67197_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/676ae9de5449/41598_2024_67197_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/59f1d8b3fc19/41598_2024_67197_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/b2b8110b95b4/41598_2024_67197_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/09a616b47912/41598_2024_67197_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1810/11266436/525be439e071/41598_2024_67197_Fig13_HTML.jpg
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