Evidence map›Paper›PMID 36532584›Full record

ArticleFrontiers in bioengineering and biotechnology2022

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization.

Zongshan Wang, Hongwei Ding, Jingjing Yang, Peng Hou, Gaurav Dhiman, Jie Wang, Zhijun Yang, Aishan Li

Abstract read
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In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Zongshan WangSchool of Information Science and Engineering, Yunnan University, Kunming, China.
Hongwei DingSchool of Information Science and Engineering, Yunnan University, Kunming, China.
Jingjing YangSchool of Information Science and Engineering, Yunnan University, Kunming, China.
Peng HouSchool of Computer Science, Fudan University, Shanghai, China.
Gaurav DhimanDepartment of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
Jie WangSchool of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China.
Zhijun YangSchool of Information Science and Engineering, Yunnan University, Kunming, China.
Aishan LiRackham Graduate School, University of Michigan, Ann Arbor, MI, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

Indexed as

benchmarkequilibrium optimizerexploration and exploitationglobal optimizationmetaheuristic algorithmsparticle swarm optimization

Identifiers

PMID36532584
PMCPMC9751665

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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.