Evidence map›Paper›PMID 39748959›Full record

ArticleHeliyon2024

Novel empress SARANI optimization algorithm for active power loss reduction and voltage stability enhancement.

Lenin Kanagasabai

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Lenin KanagasabaiPrasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes Pomarine jaeger Optimization (PJO) algorithm, Tiger hunting Optimization (THO) Algorithm, Desert Reynard and Vixen Inspired Optimization (DRVIO) Algorithm, Lonchodidae optimization (LO) algorithm, Caracal optimization (CO) algorithm, Barasingha optimization (BO) algorithm, Amur leopard optimization (AO) algorithm and Empress SARANI Optimization Algorithm to solve the active power loss reduction problem. Regular actions of Pomarine jaeger have been emulated to model the PJO procedure. In THO algorithm, how the Tiger moves to capture the prey is imitated and formulated. In DRVIO algorithm, Desert Reynard and Vixen burrowing capability and spurt tactic from desolate slayers are imitated to formulate the algorithm. LO algorithm emulates the physiognomies of convergent progression, track reliance, populace development and rivalry in the growth of the Lonchodidae populace in environment. In CO approach, Caracal assaults the designated quarry and then quests the quarry in a dashing procedure. BO algorithm stimulated by the Barasingha existence capability in the slayer subjugated atmosphere. AO algorithm imitates the Amur leopard behaviour. Movement paths, stalking, breeding and death are the some phases in the Amur leopard life cycle. Empress SARANI Optimization Algorithm is designed by integrating Parastylotermes Empress inspired optimization (PEIO) algorithm, Dryocopus martius optimization (DMO) algorithm, Ostrya Carpinifolia Search Optimization (OCSO) Algorithm, Hermitage Activities Inspired optimization (HAIO) algorithm with SARANI algorithm. Validity of Empress SARANI Optimization Algorithm is verified in 24 benchmark functions, IEEE and Practical systems. Real power loss (MW) obtained by projected algorithms for

Indexed as

Amur leopardBarasinghaCaracalDesertReynardDryocopus martiusEmpress SARANILonchodidaePomarine jaegerTiger huntingVixen

Identifiers

PMID39748959
PMCPMC11693907

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.