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

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

doi:10.1007/s11831-022-09801-z

Cited by 40 publications

(6 citation statements)

References 153 publications

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“…Many researchers have recently presented a variety of metaheuristic methods: Gray wolf optimizer (GWO) (Mirjalili et al, 2014;Pahuja, 2020), Water Cycle Algorithm (Mahdavi-Nasab et al, 2020), Shuffled Frog Leaping Algorithm (SFLA) (Gandhi and Bhattacharjya, 2020), Moth Flame Optimization (MFO) algorithm (Mirjalili, 2015;Sahoo et al, 2023), Whale Optimization Algorithm (WOA) (Mirjalili and Lewis, 2016;Dao et al, 2016), Dwarf Mongoose Optimization Algorithm (DMOA) (Agushaka et al, 2022), Cat and Mouse Based Optimizer (CMBO) (Dehghani et al, 2021), Coati Optimization Algorithm (COA) (Dehghani et al, 2023), Gazelle Optimization Algorithm (GOA) (Agushaka et al, 2023), Dragonfly Algorithm (DA) (Mirjalili, 2016), Crystal Structure Algorithm (CSA) (Khodadadi et al, 2021), and Stochastic Paint Optimizer (SPO) (Kaveh et al, 2020), which are few meta-heuristics approaches used in reliability 2.2. Complex System Optimization.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Choudhary,

Ram,

Goyal

et al. 2024

ees

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Introduction. The assessment of the reliability and cost of complex systems, such as Complex Bridge Systems (CBS) and Life Support Systems in Space Capsules (LSSSC), is fascinating. To achieve the ideal system design through diverse constraints and increase overall system reliability, researchers have extensively explored system reliability and cost optimization problems. Hence, the significant advancement in metaheuristic methods is the primary source of further system reliability and cost optimization process refinement. Aim and tasks. This research attempts to enhance the reliability and cost of complex systems named CBC and LSSSC has been presented. Results. The structure is based on few recent metaheuristic techniques, such as Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Gazelle Optimization Algorithm (GOA), Dragonfly Algorithm (DA), and Coati Optimization Algorithm (COA). Comparing the acquired findings to those found in other proposed techniques demonstrates the usefulness of a methodology based on COA. The proposed COA algorithm exhibits enhanced efficiency by offering superior solutions to reliability and cost-optimization problems. In addition, a non-parametric Friedman ranking was performed for validation. The results of this research are based on improving the reliability of the parameters and decreasing complex systems’ costs used by the five metaheuristic methods. Observing the convergence graph, Friedman ranking, statistical results test, and tables determined that COA is the most effective algorithm for a complex system’s cost and reliability parameters compared to other existing approaches, and also provided a faster solution. Conclusions. This study proposes unique ways to reduce costs while increasing parameter reliability in complex systems. After analysing the comparative solution, the authors found that when comparing these approaches (GOA, DA, MFO, WOA, and COA), the COA provided the best minimum solution for the cost and reliability of complex systems. Hence, the suggested COA procedure was more successful than that described in this study.

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Section: Metaheuristic Approachesmentioning

confidence: 99%

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Choudhary,

Ram,

Goyal

et al. 2024

ees

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“…Nadimi-Shahraki et al [14] developed the Diversity-Maintained Multi-Trial Vector Differential Evolution (DMDE) algorithm designed for large-scale global optimization problems with a Diversity Maintenance Strategy (DMS). Sahoo et al [15] presented the Migration-based Moth-Flame Optimization (M-MFO) algorithm, which incorporates migration strategies into the MFO algorithm to enhance its search capabilities. These algorithms have been successfully applied to various optimization problems, such as economic load dispatch, parameter tuning, and numerical optimization.…”

Section: Relevant Studies On Heuristics and Search Techniquementioning

confidence: 99%

Optimization and inventory management under stochastic demand using metaheuristic algorithm

Tan,

Kim,

Long

et al. 2024

PLoS ONE

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This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model’s sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.

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“…Several studies have used it to solve optimization problems [78,79]. MFO is regarded as one of the most promising metaheuristic algorithms, and it has been successfully utilized to solve optimization problems in an extensive variety of areas, including economic dispatching, engineering design, image processing, power and energy systems, and medical applications [80,81]. This algorithm's success is based on its advantages over its competitors.…”

Section: Moth Flame Optimizationmentioning

confidence: 99%

The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant

et al. 2023

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Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with a hybrid photovoltaic (PV) and wind power plant combined with a storage system. Lithium batteries and hydrogen associated with fuel cells make up this storage system. The total cost (TC) of the project over its lifetime was minimized in order to achieve the optimal sizing of the hybrid power plant components. To ensure the reliability of the new hybrid power plant, a criterion measuring the loss of power supply probability (LPSP) was implemented as a constraint. Moth Flame Optimization (MFO), Improved Grey Wolf Optimizer (I-GWO), Multi-Verse Optimizer (MVO), and African Vulture Optimization Algorithm (AVOA) were used to solve this single-objective optimization problem. The optimization techniques entailed the development of mathematical models of the components, with hourly weather data for the selected site and the output of the replaced thermal power plant serving as input data. All four algorithms produced acceptable and reasonably comparable results. However, in terms of proportion, the total cost obtained with the MFO algorithm was 0.32%, 0.40%, and 0.63% lower than the total costs obtained with the I-GWO, MVO, and AVOA algorithms, respectively. Finally, the effect of the type of storage coupled to the PV and wind systems on the overall project cost was assessed. The MFO meta-heuristic was used to compare the results for the PV–Wind–Hydrogen–Lithium Battery, PV–Wind–Hydrogen, and PV–Wind–Lithium Battery scenarios. The scenario of the PV–Wind–Hydrogen–Lithium Battery had the lowest total cost. This scenario’s total cost was 2.40% and 18% lower than the PV–Wind–Hydrogen and PV–Wind–Lithium Battery scenarios.

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

Cited by 40 publications

References 153 publications

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Optimization and inventory management under stochastic demand using metaheuristic algorithm

The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant

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