A Critical Review of Moth-Flame Optimization Algorithm and Its Variants: Structural Reviewing, Performance Evaluation, and Statistical Analysis

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(4), С. 2177 - 2225

Опубликована: Фев. 2, 2024

Язык: Английский

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Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms

Computers and Electronics in Agriculture, Год журнала: 2021, Номер 191, С. 106541 - 106541

Опубликована: Ноя. 19, 2021

Язык: Английский

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An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy, Год журнала: 2021, Номер 23(12), С. 1637 - 1637

Опубликована: Дек. 6, 2021

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward light source is an effective approach to solve global problems. However, MFO suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration exploitation. In this study, therefore, improved moth-flame (I-MFO) proposed cope with canonical MFO's locating trapped in optimum via defining memory for each moth. The tend escape taking advantage adapted wandering around search (AWAS) strategy. efficiency I-MFO evaluated CEC 2018 benchmark functions compared against other well-known metaheuristic algorithms. Moreover, obtained results are statistically analyzed Friedman test on 30, 50, 100 dimensions. Finally, ability find best optimal solutions mechanical engineering problems three latest test-suite 2020. experimental statistical demonstrate that significantly superior contender algorithms it successfully upgrades shortcomings MFO.

Язык: Английский

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Migration-Based Moth-Flame Optimization Algorithm

Processes, Год журнала: 2021, Номер 9(12), С. 2276 - 2276

Опубликована: Дек. 18, 2021

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various tasks. However, MFO cannot provide competitive results for complex problems. The sinks into the local optimum due to rapid dropping of population diversity and poor exploration. Hence, this article, migration-based moth–flame (M-MFO) proposed address mentioned issues. In M-MFO, main focus on improving position unlucky moths by migrating them stochastically early iterations using random migration (RM) operator, maintaining solution diversification storing new qualified solutions separately guiding archive, and, finally, exploiting around positions saved archive guided (GM) operator. dimensionally aware switch between these two operators guarantees convergence toward promising zones. M-MFO was evaluated CEC 2018 benchmark suite dimension 30 compared against seven well-known variants MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, ODSFMFO. Then, top four latest high-performing were considered experiments with different dimensions, 30, 50, 100. experimental evaluations proved provides exploration ability maintenance employing strategy archive. addition, statistical analyzed Friedman test performance contender algorithms used experiments.

Язык: Английский

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Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems

Expert Systems with Applications, Год журнала: 2022, Номер 215, С. 119303 - 119303

Опубликована: Ноя. 19, 2022

Язык: Английский

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Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design

Computers & Industrial Engineering, Год журнала: 2022, Номер 168, С. 108032 - 108032

Опубликована: Фев. 28, 2022

Язык: Английский

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A hybrid Harris Hawks optimizer for economic load dispatch problems

Alexandria Engineering Journal, Год журнала: 2022, Номер 64, С. 365 - 389

Опубликована: Сен. 22, 2022

This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer to tackle economic load dispatch (ELD) problems. ELD is an important problem in power systems that tackled by finding optimal schedule generation units minimize fuel conceptions under set constraints. Due complexity search space, as it rigid and deep, exploitation HHO improved hybridizing recent local method called adaptive-hill climbing. The can navigate several potential space regions, while climbing used deeply for solution each region. To evaluate proposed approach, six versions cases various complexities constraints have been which are 6 1263 MW demand, 13 1800 2520 15 2630 40 10500 140 49342 demand. Furthermore, algorithm evaluated on two real-world units-1263 15units-2630 MW. results show achieve significant performance majority experimented cases. It best-reported case when compared well-established methods. Additionally, obtains second-best 10 In conclusion, be alternative solve problems efficient.

Язык: Английский

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The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

IEEE Access, Год журнала: 2022, Номер 10, С. 19254 - 19283

Опубликована: Янв. 1, 2022

This paper presents a new hybrid metaheuristic algorithm, the Harris Hawks Optimizer-Arithmetic Optimization Algorithm (hHHO-AOA), as we have named it. It is proposed for sizing optimization and design of autonomous microgrids. The algorithm has been developed based on operating Optimizer (HHO) Arithmetic (AOA) in uniquely cooperative manner. expected to increase solution accuracy by increasing diversity during an process. performance verified with evaluation metrics well-known statistical tests. According Friedman ranking test, performs 77.9% better than HHO 78.6% AOA. Similarly, checked Wilcoxon signed-rank test revealed significant superiority compared AOA alone. Later, tested microgrid that consists photovoltaic (PV) system, wind turbine (WT) battery energy storage system (BESS), diesel generators (DGs), commercial type load. For optimal capacity planning these components, problem which loss power supply probability (LPSP) cost (COE) are defined objective function formulated. done produced lowest LPSP COE along highest rate renewable fraction (RF). In conclusion, it demonstrated hHHO-AOA proved itself designing reliable, economical, eco-friendly microgrids best way.

Язык: Английский

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Chaotic Sand Cat Swarm Optimization

Mathematics, Год журнала: 2023, Номер 11(10), С. 2340 - 2340

Опубликована: Май 17, 2023

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This combines the features of recently introduced SCSO with concept chaos. The basic aim to integrate chaos feature non-recurring locations into SCSO’s core search process improve global performance convergence behavior. Thus, randomness in can be replaced by chaotic map due similar better statistical dynamic properties. addition these advantages, low consistency, local optimum trap, inefficiency search, population diversity issues are also provided. CSCSO, several maps implemented more efficient behavior exploration exploitation phases. Experiments conducted on wide variety well-known test functions increase reliability results, as well real-world was applied total 39 multidisciplinary It found 76.3% responses compared best-developed variant other chaotic-based metaheuristics tested. extensive experiment indicates that CSCSO excels providing acceptable results.

Язык: Английский

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Lens-imaging learning Harris hawks optimizer for global optimization and its application to feature selection

Expert Systems with Applications, Год журнала: 2022, Номер 202, С. 117255 - 117255

Опубликована: Апрель 26, 2022

Язык: Английский

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