ArticleArchives of computational methods in engineering : state of the art reviews2023
Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications.
Article in Archives of computational methods in engineering : state of the art reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 78 citations in OpenAlex.
- Improved Egret Swarm Optimization Algorithm Based on Variable-Factor Weighted Learning and Adjacent Generation Dimension Crossover Strategy.Biomimetics (Basel, Switzerland) · 2026Article
- Multi-Strategy Improved Red-Billed Blue Magpie Optimization Algorithm and Its Engineering Applications.Biomimetics (Basel, Switzerland) · 2026Article
- Design and optimization of a compact dual band metal insulator metal filter for high sensitivity refractive index sensing using particle swarm optimization.Scientific reports · 2025Article
- Network lifetime improvement in wireless sensor networks using energy-efficient bat-moth flame optimization technique.Scientific reports · 2025Article
- Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment.Diagnostics (Basel, Switzerland) · 2024Article
- Optimization and inventory management under stochastic demand using metaheuristic algorithm.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.