ArticleScientific reports2025
Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- An enhanced adaptive elephant herding optimization based on hybrid cuckoo search algorithm and elite opposition-based learning.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cuckoo Search (CS) is a popular algorithm used to solve numerous challenging problems. In the present work, a novel variant of CS is presented to eliminate its shortcomings. The proposed algorithm is hybridized with the naked mole rat algorithm (NMRA) to enhance the exploitative behavior of CS, and is called Mutated Adaptive Cuckoo Search Algorithm (MaCN). This new algorithm has self-adaptive properties and its key feature is to divide the solutions into multiple sections, which are often called sub-swarms. In addition, a bare-bones search mechanism is also added to enhance exploration. The use of adaptive inertia weights helps optimize the switching probability, an important CS parameter that helps to achieve a balanced operation. The proposed MaCN algorithm is tested on CEC 2005 and CEC 2014 benchmark problems. Comparative studies showed that MaCN delivers promising results in solving CEC competition benchmark problems compared to JADE, success history-based adaptive DE (SHADE), LSHADE-SPACMA and self-adaptive DE (SaDE), among others. In addition to numerical benchmarks, MaCN is used to solve the industrial engineering frame structure and a comparison with hybridization of particle swarm with passive congregation (PSOPC), shuffled frog leaping algorithm hybrid with invasive weed optimization (SFLAIWO), particle swarm ant colony optimization (PSOACO), early strategy with DE (ES-DE), and others show its superiority. In addition, the Wilcoxon rankum and the Freidmann test statistically prove the significance of the proposed MaCN algorithm. MaCN was found to score first rank for the benchmarks. The application of the MaCN algorithm to solve the design problems of the suggests that the best new results are obtained for all test cases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.