Shuffled Differential Evolution-Based Combined Heat and Power Economic Dispatch

Nagaraju, S. Siva; Reddy, A. Srinivasa; Vaisakh, K.

doi:10.1007/978-981-13-0514-6_51

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…To perform the comparative analysis, the new variant have been compared with standard GSA and nine variants of GSA, namely OGSA [50], mDGSA [61], GGSA [56], PGSA [59], MGSA(I) [57], MGSA(II) [58], EGSA(I) [51], EGSA(II) [52], and EGSA(III) [53]. The comparison also includes the performance results with three recent metaheuristic algorithms, namely modified particle swarm optimization (MPSO) [79], shuffled differential evolutionary (SDE) [80], and spiral biogeography based optimizer (SBBO) [81]. Moreover, the algorithms are evaluated over 10, 30, 50, and 90 dimensions for each benchmark function.…”

Section: Resultsmentioning

confidence: 99%

A new clustering method for the diagnosis of CoVID19 using medical images

et al. 2021

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With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score. Keywords CoVID19 diagnosis • Clustering • Metaheuristic algorithm • Gravitational search algorithm 1 Introduction With the rapid outbreak of the Corona virus Disease (CoVID19) around the globe [1], the World health organization (WHO) has been declared CoVID19 as a Public Health Emergency of International Concern (PHEIC) [2]. Pathogenically, the virus identified in CoVID19 is "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) which is quite distinct from other respiratory viruses like MERS-CoV, SARS-CoV, avian influenza, and influenza [3]. Usual symptoms in a COVID19 patient are fever, dry cough, and tiredness. Other signs experienced by patients include sore throat, diarrhea, headache, loss of taste

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Section: Resultsmentioning

confidence: 99%

A new clustering method for the diagnosis of CoVID19 using medical images

et al. 2021

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Gravitational search algorithm: a comprehensive analysis of recent variants

Mittal

Tripathi

Pandey

et al. 2020

Multimed Tools Appl

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Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

Maaroof

Rashid

Abdulla

et al. 2022

Arch Computat Methods Eng

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Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application.

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Shuffled Differential Evolution-Based Combined Heat and Power Economic Dispatch

Cited by 5 publications

References 14 publications

A new clustering method for the diagnosis of CoVID19 using medical images

A new clustering method for the diagnosis of CoVID19 using medical images

Gravitational search algorithm: a comprehensive analysis of recent variants

Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

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