Evidence map›Paper›PMID 42345696›Full record

ArticleBiomimetics (Basel, Switzerland)2026

Enhanced Philoponella Prominens Optimization (EESPPO) Algorithm Integrated with Experience Exchange Strategy for Global Optimization and Engineering Design Problems.

Zhongzhen Yan, Yi Yu, Yuan Cao, Jie Gao

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2026. 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

4 authors.

Zhongzhen YanSchool of Computer Science and Artificial Intelligence, Hubei University of Technology, Wuhan 430068, China.
Yi YuSchool of Computer Science and Artificial Intelligence, Hubei University of Technology, Wuhan 430068, China.
Yuan CaoSchool of Computer Science and Artificial Intelligence, Hubei University of Technology, Wuhan 430068, China.
Jie GaoSchool of Computer Science and Artificial Intelligence, Hubei University of Technology, Wuhan 430068, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and collective information interaction behaviors of

Indexed as

bio-inspired meta-heuristicsCEC2017 benchmarkengineering design optimizationexperience exchange strategyglobal optimizationPhiloponella Prominens Optimization (PPO)population diversity

Identifiers

PMID42345696
PMCPMC13297547

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.