A Hybrid Moth Flame Optimization Algorithm for Global Optimization

Sahoo, Saroj Kumar; Saha, Apu Kumar

doi:10.1007/s42235-022-00207-y

Cited by 38 publications

(14 citation statements)

References 75 publications

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“…In Sahoo and Saha [ 60 ], the authors proposed a new hybrid metaheuristic algorithm called h-MFOBOA by integrating MFO and BOA. To enhance the performance and solution quality of the MFO algorithm, the authors added the global and local phases of BOA after the position update phase of the MFO algorithm.…”

Section: Variants Of Mfo Algorithmmentioning

confidence: 99%

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

Sahoo

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

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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.

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Section: Variants Of Mfo Algorithmmentioning

confidence: 99%

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

Sahoo

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

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“…As real-world optimization problems have grown in complexity and difficulty over the last several decades, the need for optimization methods has become more apparent. The performance of multi-modal, non-continuous, and non-differentiable issues is well-served by the meta-heuristic algorithm family of stochastic search approaches [ 3 ]. Two primary categories may be used to classify optimization approaches: gradient-based techniques and Mas [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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“…Ghafori and Gharehchopogh [ 18 ] conducted a comprehensive survey of Spotted Hyena Optimizer (SHO) and obtained the direction of improvement of the SHO algorithm. Meta-heuristic algorithms can provide feasible results for highly complex constrained problems, i.e., Grey Wolf Optimizer (GWO) [ 19 ], Harris Hawks Optimization (HHO) [ 20 ], Flower Pollination Algorithm (FPA) [ 21 ], Social Network Search (SNS) [ 22 ], Gravitational Search Algorithm (GSA) [ 23 ], Dragonfly Algorithm (DA) [ 24 ], Alpine Skiing Optimization (ASO) [ 25 ], Elite Opposition-based Learning and Chaotic k -best Gravitational Search Strategy based Grey Wolf Optimizer (EOCSGWO) [ 26 ], Artificial Bee Colony (ABC) [ 27 ], Teaching–Learning-Based Optimization (TLBO) [ 28 ], hybrid Moth Flame Optimization and Butterfly Optimization Algorithm (h-MFOBOA) [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications

et al. 2023

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Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.

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A Hybrid Moth Flame Optimization Algorithm for Global Optimization

Cited by 38 publications

References 75 publications

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

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

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications

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