• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种改进的多目标优化问题农田肥力算法。

An improved farmland fertility algorithm for many-objective optimization problems.

机构信息

School of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China.

China Unicom in Changchun City Branch, Changchun, 130000, China.

出版信息

Sci Rep. 2022 Feb 15;12(1):2545. doi: 10.1038/s41598-022-06329-x.

DOI:10.1038/s41598-022-06329-x
PMID:35169153
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8847583/
Abstract

Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, difficulties in scalability upon MaOPs including the selection of leaders, etc., are encountered because the most evolutionary algorithms are proposed for single-objective optimization. To further improve the performance of many-objective evolutionary algorithms in solving MaOPs when the number of the objectives increases, this paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). In MOIFF, a novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy. In order to handle MaOPs effectively, FF has been tailored from the following aspects. An individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual; a novel method based on individual cumulative ranking value to constitute and update the global memory and local memory of each individual is proposed, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on MaOPs.

摘要

最近对多目标优化问题（MaOPs）的研究倾向于采用一些具有出色收敛精度和速度的有前途的进化算法。然而，由于大多数进化算法是针对单目标优化提出的，因此在 MaOPs 中遇到了可扩展性方面的困难，例如领导者的选择等。为了进一步提高多目标进化算法在解决 MaOPs 时的性能，当目标数量增加时，本文提出了一种基于改进的农田肥力算法（MOIFF）的多目标优化算法。在 MOIFF 中，采用了一种新颖的基于生物启发的元启发式方法，称为农田肥力算法（FF），作为优化策略。为了有效地处理 MaOPs，FF 从以下几个方面进行了调整。提出了一种基于累积排序值的个体适应度评估方法，以区分每个个体的质量；提出了一种基于个体累积排序值的新方法来构成和更新每个个体的全局记忆和局部记忆，并设计了混合子空间搜索和全空间搜索方法来更新土壤优化和土壤融合阶段的个体。此外，提出了自适应环境选择。最后，将 MOIFF 与四种最先进的多目标进化算法在具有不同特征的多个测试问题上进行了比较，包括 DTLZ 和 WFG 测试套件。实验结果表明，所提出的算法在 MaOPs 上具有竞争力的收敛性和多样性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/1fdfcd4c6117/41598_2022_6329_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/2260b4aee125/41598_2022_6329_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/bffaed6fa032/41598_2022_6329_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/3ea8c8558e79/41598_2022_6329_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/1a1c4e78ae89/41598_2022_6329_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/79f0db756511/41598_2022_6329_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/deaeda09a6f3/41598_2022_6329_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/adc9c8007552/41598_2022_6329_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/3e2c4a901368/41598_2022_6329_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/1fdfcd4c6117/41598_2022_6329_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/2260b4aee125/41598_2022_6329_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/bffaed6fa032/41598_2022_6329_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/3ea8c8558e79/41598_2022_6329_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/1a1c4e78ae89/41598_2022_6329_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/79f0db756511/41598_2022_6329_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/deaeda09a6f3/41598_2022_6329_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/adc9c8007552/41598_2022_6329_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/3e2c4a901368/41598_2022_6329_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37b7/8847583/1fdfcd4c6117/41598_2022_6329_Fig9_HTML.jpg

相似文献

1
An improved farmland fertility algorithm for many-objective optimization problems.一种改进的多目标优化问题农田肥力算法。
Sci Rep. 2022 Feb 15;12(1):2545. doi: 10.1038/s41598-022-06329-x.
2
A Subregion Division-Based Evolutionary Algorithm With Effective Mating Selection for Many-Objective Optimization.基于子区域划分的有效交配选择进化算法用于多目标优化。
IEEE Trans Cybern. 2020 Aug;50(8):3477-3490. doi: 10.1109/TCYB.2019.2906679. Epub 2019 Apr 11.
3
Many-objective African vulture optimization algorithm: A novel approach for many-objective problems.多目标非洲秃鹫优化算法：一种解决多目标问题的新方法。
PLoS One. 2023 May 17;18(5):e0284110. doi: 10.1371/journal.pone.0284110. eCollection 2023.
4
Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization.基于生物地理学/复数算法的多目标优化分解自适应权重调整方法。
PLoS One. 2020 Oct 9;15(10):e0240131. doi: 10.1371/journal.pone.0240131. eCollection 2020.
5
Evolution Strategy-Based Many-Objective Evolutionary Algorithm Through Vector Equilibrium.基于向量均衡的基于进化策略的多目标进化算法
IEEE Trans Cybern. 2021 Nov;51(11):5455-5467. doi: 10.1109/TCYB.2019.2960039. Epub 2021 Nov 9.
6
Evolutionary path control strategy for solving many-objective optimization problem.用于解决多目标优化问题的进化路径控制策略。
IEEE Trans Cybern. 2015 Apr;45(4):702-15. doi: 10.1109/TCYB.2014.2334632. Epub 2014 Jul 18.
7
A Decomposition-Based Many-Objective Artificial Bee Colony Algorithm.基于分解的多目标人工蜂群算法。
IEEE Trans Cybern. 2019 Jan;49(1):287-300. doi: 10.1109/TCYB.2017.2772250. Epub 2017 Nov 28.
8
Many-objective BAT algorithm.多目标蝙蝠算法。
PLoS One. 2020 Jun 11;15(6):e0234625. doi: 10.1371/journal.pone.0234625. eCollection 2020.
9
A Constrained Many-Objective Optimization Evolutionary Algorithm With Enhanced Mating and Environmental Selections.一种具有增强交配和环境选择的约束多目标优化进化算法。
IEEE Trans Cybern. 2023 Aug;53(8):4934-4946. doi: 10.1109/TCYB.2022.3151793. Epub 2023 Jul 18.
10
An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization.一种基于闵可夫斯基距离的多目标优化进化算法。
IEEE Trans Cybern. 2019 Nov;49(11):3968-3979. doi: 10.1109/TCYB.2018.2856208. Epub 2018 Jul 30.

引用本文的文献

1
A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem.一种基于三种状态的多目标进化算法，用于求解多目标优化问题。
Sci Rep. 2024 Aug 19;14(1):19140. doi: 10.1038/s41598-024-70145-8.

本文引用的文献

1
The hybrid bacterial foraging algorithm based on many-objective optimizer.基于多目标优化器的混合细菌觅食算法。
Saudi J Biol Sci. 2020 Dec;27(12):3743-3752. doi: 10.1016/j.sjbs.2020.08.021. Epub 2020 Aug 19.
2
Many-objective BAT algorithm.多目标蝙蝠算法。
PLoS One. 2020 Jun 11;15(6):e0234625. doi: 10.1371/journal.pone.0234625. eCollection 2020.
3
HypE: an algorithm for fast hypervolume-based many-objective optimization.HypE：一种基于快速超体积的多目标优化算法。
Evol Comput. 2011 Spring;19(1):45-76. doi: 10.1162/EVCO_a_00009. Epub 2010 Jul 22.