Evidence map›Paper›PMID 39851763›Full record

ArticleBiomimetics (Basel, Switzerland)2025

Multi-Strategy Improved Whale Optimization Algorithm and Its Engineering Applications.

Yu Zhou, Zijun Hao

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2025. 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
  3. 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

2 authors.

Yu ZhouSchool of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.
Zijun HaoSchool of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.

Funding

The Natural Science Fund of Ningxia No. 2022AAC03235
6 · The paper itself

Abstract

The Whale Optimization Algorithm (WOA) is recognized for its simplicity, few control parameters, and effective local optima avoidance. However, it struggles with global search efficiency and slow convergence. This paper introduces the Improved WOA (ImWOA) to overcome these challenges. Initially, ImWOA utilizes a dynamic elastic boundary optimization strategy, which leverages boundary information and the current optimal position to guide solutions that exceed the boundaries back within permissible limits, gradually converging towards the optimal solution. Subsequently, ImWOA integrates an advanced random searching strategy that equilibrates global and local searches by focusing on the current optimal location and the mean position of all individuals. Lastly, a combined mutation mechanism is employed to enhance population diversity, prevent the algorithm from stagnating in local optima, and consequently augment its overall search capability. Performance evaluations on CEC2017 benchmark functions show ImWOA outperforming five metaheuristic algorithms and three WOA variants in optimization accuracy, stability, and convergence speed. ImWOA excelled in 25 out of 29 test functions in 30D and 26 out of 29 in 100D scenarios. Furthermore, its efficacy in addressing complex challenges is corroborated by real-world applications in reducer design, vehicle side impact design, and welded beam design, highlighting its potential utility across various engineering domains.

Indexed as

combined mutation mechanismdynamic elastic boundary optimization strategyimproved random searching strategyimproved whale optimization algorithm

Identifiers

PMID39851763
PMCPMC11762550

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.