Moth Search: Variants, Hybrids, and Applications

Li, Juan; Yang, Yuanhua; An, Qing; Lei, Hong; Deng, Qian; Wang, Gai-Ge

doi:10.3390/math10214162

Cited by 7 publications

(3 citation statements)

References 123 publications

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“…All applicable government requirements have been maintained in order to facilitate a smooth implementation of the study. In cases where the information collected is not harmful to society, it is recommended that it be shared via public rather than private channels [18]. This firm Students take safety measures to reduce the risk of injury to themselves and others as a result of their participation.…”

Section: E Ethical Considerationmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings.

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Section: E Ethical Considerationmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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“…Mimicking the oscillatory behaviour observed in slime moulds, SMA utilizes heuristics informed by experiences within specific regions (or patches) to decide when to move on, mirroring the natural behaviour of these organisms 30 . The Moth Search Algorithm derives its inspiration from the behaviour of moths drawn to light sources, employing the principle of phototaxis to guide its optimization process 31 . Similarly, the Hunger Games Search algorithm takes inspiration from the survival of the fittest concept depicted in the Hunger Games series, employing competitive selection processes to seek optimal solutions 32 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Mohamed,

Mahmoud,

Saied

et al. 2024

Sci Rep

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The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

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“…The Slime Mold Algorithm, derived from the natural oscillation patterns of slime molds, has developed into a stochastic optimizer renowned for its exceptional exploration abilities and exploitation tendencies [23]. The Moth Search Algorithm explores engineering optimization and applications based on the most representative features of moths, such as moth flight and phototaxis [24]. The Colony Predation Algorithm formulates stochastic optimization strategies by emulating the hunting tactics employed by animal groups, wherein boundary values are substituted with optimal position values across boundaries to augment the algorithm's exploitation prowess [25].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization

Yu,

Chang,

Lee

2024

Biomimetics

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This study introduces an improved quantum-behavior particle swarm optimization (IQPSO), tailored for the task of maximum power point tracking (MPPT) within photovoltaic generation systems (PGSs). The power stage of the MPPT system comprises a series of buck-boost converters, while the control stage contains a microprocessor executing the biomimetic algorithm. Leveraging the series buck-boost converter, the MPPT system achieves optimal operation at the maximum power point under both ideal ambient conditions and partial shade conditions (PSCs). The proposed IQPSO addresses the premature convergence issue of QPSO, enhancing tracking accuracy and reducing tracking time by estimating the maximum power point and adjusting the probability distribution. Employing exponential decay, IQPSO facilitates a reduction in tracking time, consequently enhancing convergence efficiency and search capability. Through single-peak experiments, multi-peak experiments, irradiance-changing experiments, and full-day experiments, it is demonstrated that the tracking accuracy and tracking time of IQPSO outperform existing biomimetic algorithms, such as the QPSO, firefly algorithm (FA), and PSO.

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Moth Search: Variants, Hybrids, and Applications

Cited by 7 publications

References 123 publications

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization

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