A Dual-Mechanism Enhanced Secretary Bird Optimization Algorithm and Its Application in Engineering Optimization.

Changzu Chen, Li Cao, Binhe Chen, Yaodan Chen, Xinxue Wu

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 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

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

1 citing paper in PubMed.

Sunn Pest Optimization (SPO): A Phase-Differentiated Bio-Inspired Metaheuristic for Global Optimization with Application to Precision Agriculture Monitoring Networks.Biomimetics (Basel, Switzerland) · 2026
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

5 authors.

Changzu ChenSchool of Electronics and Electrical Engineering, Wenzhou University of Technology, Wenzhou 325035, China.ORCID 0009-0005-8130-3920

Li CaoSchool of Electronics and Electrical Engineering, Wenzhou University of Technology, Wenzhou 325035, China.ORCID 0000-0003-0728-5048

Binhe ChenSchool of Electronics and Electrical Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

Yaodan ChenSchool of Electronics and Electrical Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

Xinxue WuWenzhou Branch Data Research Center, China Tower Corporation Limited, Wenzhou 325000, China.

Funding

the Natural Science Foundation of Zhejiang Province LY23F010002the School-level Scientific Research Project of Wenzhou university of Technology ky202403

6 · The paper itself

Abstract

The secretary bird optimization algorithm is a recently developed swarm intelligence method with potential for solving nonlinear and complex optimization problems. However, its performance is constrained by limited global exploration and insufficient local exploitation. To address these issues, an enhanced variant, ORSBOA, is proposed by integrating an optimal neighborhood perturbation mechanism with a reverse learning strategy. The algorithm is evaluated on the CEC2019 and CEC2022 benchmark suites as well as four classical engineering design problems. Experimental results demonstrate that ORSBOA achieves faster convergence, stronger robustness, and higher solution quality than nine state-of-the-art algorithms. Statistical analyses further confirm the significance of these improvements, validating the effectiveness and applicability of ORSBOA in solving complex optimization tasks.

Indexed as

engineering optimizationoptimal neighborhood perturbationreverse learning strategysecretary bird optimization algorithmswarm intelligence

Identifiers

PMID41149209

DOI10.3390/biomimetics10100679

PMCPMC12563819

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.