Evidence map›Paper›PMID 39451845›Full record

ArticleBiomimetics (Basel, Switzerland)2024

Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA.

Chunfang Li, Yuqi Yao, Mingyi Jiang, Xinming Zhang, Linsen Song, Yiwen Zhang, Baoyan Zhao, Jingru Liu, Zhenglei Yu, Xinyang Du and 1 more

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

11 authors.

Chunfang LiSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Yuqi YaoSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Mingyi JiangThe People's Liberation Army (PLA) Unit 63850 of China, Changchun 130022, China.
Xinming ZhangSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0000-0002-6713-1430
Linsen SongSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Yiwen ZhangSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0009-0005-7387-917X
Baoyan ZhaoFAW Tooling Die Manufacturing Co., Ltd., Changchun 130013, China.
Jingru LiuFAW Tooling Die Manufacturing Co., Ltd., Changchun 130013, China.
Zhenglei YuCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
Xinyang DuFAW Tooling Die Manufacturing Co., Ltd., Changchun 130013, China.
Shouxin RuanChina FAW Group Corporation, Changchun 130000, China.

Funding

Jilin Province Science and Technology Department 20230301005ZD
6 · The paper itself

Abstract

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm's global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm's efficiency and robustness. Ultimately, an extensive validation of MISWOA through "simulation + experimentation" approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.

Indexed as

adaptive spiral indentation strategyglobal search capabilityMulti-Swarm Optimizationoptimization algorithm robustnessWhale Optimization Algorithm

Identifiers

PMID39451845
PMCPMC11506474

What OpenQuestion holds

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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.