Optimal Positioning of Distributed Generator using Hybrid Optimization algorithm in Radial Distribution System

Ravikumar, Surej; Vennila, H.; Deepak, R

doi:10.46253/jcmps.v2i4.a1

Cited by 5 publications

(1 citation statement)

References 13 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Primarily, by assembly subsets recognition, the minimizations of iteration are attained, and at each phase, the fitness is evaluated. This technique eradicates the subsets that have maximum fitness value amid the reasons for the minimization in iteration count [15]. In numerous studies, the conventional Dragonfly algorithm (DA) [13] is used because of its ease and important static and dynamic behavior.…”

Section: Proposed Model: Hybrid Lion and Dragonfly Approachmentioning

confidence: 99%

Hybridizing Lion and Dragonfly Algorithm for Robotic Assembly subsets recognition model

Lakshmi¹

2022

JCMPS

View full text Add to dashboard Cite

Nowadays, a lot of interrelated shape products have been proposed by manufacturing industries for diverse applications in diverse fields like aerospace, defense, as well as space centers. Because of assembly operation, 30 percent of time utilization occurs in manufacturing while comparing with the residual processes in manufacturing. Attaining the optimal sequence is highly complex due to the assembly sequence planning which is a multi-model optimization issue. The probable number of sequences increases exponentially as the number of parts in the assembly raises consequently attaining the optimal assembly sequences which are very complex and have time utilization. To attain the assembly sequences, there subsist numerous mathematical approaches. Nevertheless, current researches affirm that they carry out poorly when it comes to multi-objective optimal assembly sequence. Nowadays, numerous studies have worked on various soft computingbased approaches to solve assembly sequence issues. Here, the assembly subset recognition model is developed. The developed model is used initially to resolve the assembly sequence issues. This approach eradicates aforesaid assembly sets which have numerous directional changes and need high energy. Here, the Lion Mutated and Updated Dragon Algorithm (LMU-DA) is proposed, which is the theoretical hybridization of the Lion Algorithm (LA) and Dragonfly Algorithm (DA). This technique is evaluated with the conventional approaches and it is attained to be efficient in obtaining the optimal assembly sequence for an industrial product.

show abstract

Section: Proposed Model: Hybrid Lion and Dragonfly Approachmentioning

confidence: 99%

Hybridizing Lion and Dragonfly Algorithm for Robotic Assembly subsets recognition model

Lakshmi¹

2022

JCMPS

View full text Add to dashboard Cite

show abstract

Contextual Features and Optimal Hierarchical Attention Networks for Sentiment Classification Under Data Streaming Environment

Mohana¹,

Kalaiselvi²,

Sasipriyaa³

2022

Mobile Netw Appl

View full text Add to dashboard Cite

Optimal block search mechanism using deep recurrent neural network for enabling the code-efficiency in HEVC

Korishetti¹,

Malemath

2021

JEDT

View full text Add to dashboard Cite

Purpose High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications. Design/methodology/approach In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME. Findings The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827. Originality/value In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

show abstract

Optimal Positioning of Distributed Generator using Hybrid Optimization algorithm in Radial Distribution System

Cited by 5 publications

References 13 publications

Hybridizing Lion and Dragonfly Algorithm for Robotic Assembly subsets recognition model

Hybridizing Lion and Dragonfly Algorithm for Robotic Assembly subsets recognition model

Contextual Features and Optimal Hierarchical Attention Networks for Sentiment Classification Under Data Streaming Environment

Optimal block search mechanism using deep recurrent neural network for enabling the code-efficiency in HEVC

Contact Info

Product

Resources

About