Merging slime mould with whale optimization algorithm for optimal allocation of hybrid power flow controller in power system

Bhandakkar, Anjali Atul; Mathew, Lini

doi:10.1080/0952813x.2022.2040598

Cited by 6 publications

(6 citation statements)

References 46 publications

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“…The best placement of hybrid power flow controllers (HPFC) has been accomplished with hybrid SMA-WOA [ 17 ]. Integrated SMA, which has been offered as a method, will be put into practice (ISMA).…”

Section: Methods Of Smamentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Methods Of Smamentioning

confidence: 99%

“…The SMA is a population-based meta-heuristic algorithm recently introduced by Li et al [ 17 ]. The SMA takes on the shape of the acellular Slime Mould Physarum polycephalum while actively searching for a source of nutrition.…”

Section: Slime Mould Algorithmmentioning

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

“…Anji Reddy Vaka et al [91] proposed a hybrid SMA with WOA, where the WOA technique performed an exploration in the first half of the iterations and the SMA method performed an exploitation in the second half of the iterations. Bhandakkar AA et al [92] improved the searching behavior of an SMA by incorporating it with WOA. Li X et al [93] combined an SMA and an WOA with two fusion measures to enhance the global optimization ability.…”

Section: Hybridization With the Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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“…Bhandakkar and Mathew [ 226 ] proposed the ISMA algorithm by hybridizing WOA with a slime mould algorithm (SMA) for optimal allocation in a hybrid power flow controller (HPFC). ISMA uses the position updating strategy of the WOA to enhance the search strategy of SMA.…”

Section: Hybrid Variants Of Woamentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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Merging slime mould with whale optimization algorithm for optimal allocation of hybrid power flow controller in power system

Cited by 6 publications

References 46 publications

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

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

Advances in Slime Mould Algorithm: A Comprehensive Survey

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

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