A Modified Slime Mould Algorithm for Global Optimization

Tang, Andi; Tang, Suhua; Han, Tong; Huan, Zhiguang; Xie, Lei

doi:10.1155/2021/2298215

Cited by 38 publications

(20 citation statements)

References 64 publications

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“…There is a suggestion for an enhanced variation of SMA called MSMA [ 48 ]. A chaotic antagonistic learning approach is used to increase population diversity.…”

Section: Methods Of Smamentioning

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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“…There is a suggestion for an enhanced variation of SMA called MSMA [ 48 ]. A chaotic antagonistic learning approach is used to increase population diversity.…”

Section: Methods Of Smamentioning

confidence: 99%

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

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Among the swarm intelligence algorithms, the slime mould algorithm (SMA) was put forward by Li et al [37] and other scholars in 2020. It mainly simulates the spread activity and looks for the food behavior of slime molds [38]. Te adaptive weight afects the propagation wave of slime molds on the basis of organism oscillators, thereby generating positive and inverse feedbacks, which is an optimal connection path with better exploration ability and development tendency [39].…”

Section: Support Vector Machine Algorithm Based On Smamentioning

confidence: 99%

Research on Supply Chain Financial Risk Prevention Based on Machine Learning

Lei

Qiaoming

Zhao

2023

Computational Intelligence and Neuroscience

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Artificial intelligence (AI) proves decisive in today’s rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

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“…The three main repetitive process carried for the optimization are the approach, wrap and oscillation. The mathematical expressions of the three processes are discussed elaborately in [23,24]. In this article, the technicality of the SMA is equivalently designed in correlation with MPPT controller to fine the optimized duty cycle value when the PSC occurs.…”

Section: Briefing Of Smamentioning

confidence: 99%

Experimental Investigation of efficiency enhancement in Solar Photovoltaic Systems under Partial Shading Conditions using Discrete Time Slime Mould Optimization

Shunmugalatha

2023

Preprint

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The Solar PhotoVoltaic (SPV) systems are the trending and commercially reputable power source abundantly served by the nature to the mankind. Partial Shading Conditions (PSC) are one of the critical concepts in the SPV maximum power extraction. PSC’s are nonlinear and fuzzy in its attributes, as it is unpredictable. Hence, it has numerous Local Maximum Peak Power (LMPP) points. Although, a wide spread of Maximum Power Point Tracking (MPPT) algorithms are doing justice in locating the peak power points and stabilize the system, they are inadequate to locate the LMPP’s and the Global Maximum Peak Power (GMPP) point. This paper proposes a discrete time-based Slime Mould Optimization (SMO), providing an effective support to the buck converter based MPPT controller for SPV systems. The analysis and testament of buck converter in discrete domain alleviates the optimization in discrete samples, which accelerates the computation speed in locating the LMPP and GMPP. The proposed methodology is validated from the predominant parametric results like tracking time, power efficiency and the stability of the system under various PSC’s. The experimental implementations are performed in MATLAB simulations and experimented with dSPACE-MicroLabBox.

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A Modified Slime Mould Algorithm for Global Optimization

Cited by 38 publications

References 64 publications

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

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

Research on Supply Chain Financial Risk Prevention Based on Machine Learning

Experimental Investigation of efficiency enhancement in Solar Photovoltaic Systems under Partial Shading Conditions using Discrete Time Slime Mould Optimization

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