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蛾火焰优化算法：理论、改进、杂交及应用

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications.

作者信息

Sahoo Saroj Kumar, Saha Apu Kumar, Ezugwu Absalom E, Agushaka Jeffrey O, Abuhaija Belal, Alsoud Anas Ratib, Abualigah Laith

机构信息

Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India.

School of Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa.

出版信息

Arch Comput Methods Eng. 2023;30(1):391-426. doi: 10.1007/s11831-022-09801-z. Epub 2022 Aug 29.

DOI:10.1007/s11831-022-09801-z
PMID:36059575
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9422949/
Abstract

The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and energy systems, engineering design, economic dispatch, image processing, and medical applications. A comprehensive review of MFO variants is presented in this context, including the classic version, binary types, modified versions, hybrid versions, multi-objective versions, and application part of the MFO algorithm in various sectors. Finally, the evaluation of the MFO algorithm is presented to measure its performance compared to other algorithms. The main focus of this literature is to present a survey and review the MFO and its applications. Also, the concluding remark section discusses some possible future research directions of the MFO algorithm and its variants.

摘要

蛾火优化（MFO）算法属于群体智能家族，被应用于解决众多领域中的复杂现实世界优化问题。MFO及其变体易于理解且操作简单。然而，这些算法已成功解决了不同领域的优化问题，如电力和能源系统、工程设计、经济调度、图像处理及医学应用。在此背景下，对MFO变体进行了全面综述，包括经典版本、二进制类型、改进版本、混合版本、多目标版本以及MFO算法在各个领域的应用部分。最后，给出了MFO算法的评估，以衡量其与其他算法相比的性能。本文献的主要重点是对MFO及其应用进行综述。此外，结论部分讨论了MFO算法及其变体未来一些可能的研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/374ec820d704/11831_2022_9801_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/374ec820d704/11831_2022_9801_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/f1de7b406f41/11831_2022_9801_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/49d058dd967c/11831_2022_9801_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/1720cfb304f8/11831_2022_9801_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/fc85b61ab694/11831_2022_9801_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/fabaff7e576f/11831_2022_9801_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/1ce630894034/11831_2022_9801_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/24bee4f1a7fc/11831_2022_9801_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/a44db27d3771/11831_2022_9801_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/723af01252c0/11831_2022_9801_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/37cf31edc7f6/11831_2022_9801_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/b25a54e3e36b/11831_2022_9801_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a48/9422949/374ec820d704/11831_2022_9801_Fig12_HTML.jpg

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