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二存档多目标布谷鸟搜索算法在结构优化中的应用。

Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization.

作者信息

Tejani Ghanshyam G, Mashru Nikunj, Patel Pinank, Sharma Sunil Kumar, Celik Emre

机构信息

Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, 320315, Taiwan.

Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan.

出版信息

Sci Rep. 2024 Dec 30;14(1):31553. doi: 10.1038/s41598-024-82918-2.

DOI:10.1038/s41598-024-82918-2
PMID:39738304
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11685762/
Abstract

The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously. MOCS2arc is an advanced version of the traditional Multi-Objective Cuckoo Search (MOCS) algorithm, enhanced through a dual archive strategy that significantly improves solution diversity and optimization performance. To evaluate the effectiveness of MOCS2arc, we conducted extensive comparisons with several established multi-objective optimization algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, and MOCS. Such a comparison has been made with various performance metrics to compare and benchmark the efficacy of the proposed algorithm. These metrics comprehensively assess the algorithms' abilities to generate diverse and optimal solutions. The statistical results demonstrate the superior performance of MOCS2arc, evidenced by enhanced diversity and optimal solutions. Additionally, Friedman's test & Wilcoxon's test corroborate the finding that MOCS2arc consistently delivers superior optimization results compared to others. The results show that MOCS2arc is a highly effective improved algorithm for multi-objective truss structure optimization, offering significant and promising improvements over existing methods.

摘要

该研究提出了一种更好的多目标优化方法，称为双存档多目标布谷鸟搜索算法（MOCS2arc）。然后将其用于改进八个经典桁架结构和六个ZDT测试函数。优化目标是同时最小化质量和柔度。MOCS2arc是传统多目标布谷鸟搜索算法（MOCS）的高级版本，通过双存档策略进行了增强，显著提高了解的多样性和优化性能。为了评估MOCS2arc的有效性，我们与几种既定的多目标优化算法进行了广泛比较：MOSCA、MODA、MOWHO、MOMFO、MOMPA、NSGA-II、DEMO和MOCS。通过各种性能指标进行了这样的比较，以比较和衡量所提出算法的功效。这些指标全面评估了算法生成多样化和最优解的能力。统计结果证明了MOCS2arc的卓越性能，表现为多样性增强和最优解。此外，弗里德曼检验和威尔科克森检验证实了这一发现，即与其他算法相比，MOCS2arc始终能提供更优的优化结果。结果表明，MOCS2arc是一种用于多目标桁架结构优化的高效改进算法，与现有方法相比有显著且有前景的改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/defabbc4fbf1/41598_2024_82918_Fig18_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/43a9f1372ab5/41598_2024_82918_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/5c808bb42567/41598_2024_82918_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/5670fadcff2e/41598_2024_82918_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/c30ac981d46d/41598_2024_82918_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/d2a71c5acda8/41598_2024_82918_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/774c90e6742c/41598_2024_82918_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/6917206afdb6/41598_2024_82918_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/8d1719da8a08/41598_2024_82918_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c697/11685762/79ad61fdc0ff/41598_2024_82918_Fig16_HTML.jpg
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