Improved Black-Winged Kite Algorithm with Multi-Strategy Optimization for Identifying Dendrobium huoshanense DOI Creative Commons
Chaochuan Jia,

Ting Yang,

Maosheng Fu

и другие.

Biomimetics, Год журнала: 2025, Номер 10(4), С. 226 - 226

Опубликована: Апрель 4, 2025

An improved black-winged kite algorithm with multiple strategies (BKAIM) is proposed in this paper to address two critical limitations the original optimization (BKA): restricted search capability caused by low-quality initial population and reduced diversity resulting from blind following behavior during migration phase. Our enhancement implements three strategic modifications across different stages. During initialization, an opposition-based learning strategy was incorporated generate a higher-quality population. For phase, differential mutation integrated facilitate information exchange among members, mitigate tendency of leader-following behavior, enhance convergence precision, achieve optimal balance between exploration exploitation capabilities. Regarding boundary handling, conventional absorption method replaced random approach increase subsequently improve algorithm’s Comprehensive testing conducted on four benchmark function sets (CEC2017, CEC2019, CEC2021, CEC2022) validate effectiveness algorithm. Detailed analysis Wilcoxon rank-sum test comparisons other algorithms demonstrated BKAIM’s superior performance robustness. Furthermore, support vector machine (SVM) model optimized BKAIM for grade identification Dendrobium huoshanense based near-infrared spectral data, thereby confirming its practical applications.

Язык: Английский

An Improved Crayfish Optimization Algorithm: Enhanced Search Efficiency and Application to UAV Path Planning DOI Open Access
Qinyuan Huang,

Yuqi Sun,

CongBao Kang

и другие.

Symmetry, Год журнала: 2025, Номер 17(3), С. 356 - 356

Опубликована: Фев. 26, 2025

The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, recently proposed crayfish algorithm (COA) has garnered significant attention promising and noteworthy alternative. Nevertheless, COA’s search efficiency tends to diminish in later stages process, making it prone premature convergence into local optima. To address this limitation, an improved COA (ICOA) is proposed. enhance quality initial individuals ensure greater population diversity, utilizes chaotic mapping conjunction with stochastic inverse learning strategy generate population. This modification aims broaden exploration scope higher-quality regions, enhancing algorithm’s resilience against optima entrapment significantly boosting its effectiveness. Additionally, nonlinear control parameter incorporated adaptivity. Simultaneously, Cauchy variation applied population’s optimal individuals, strengthening ability overcome stagnation. ICOA’s performance evaluated by employing IEEE CEC2017 benchmark function for testing purposes. Comparison results reveal that ICOA outperforms other terms efficacy, especially when complex spatial configurations real-world problem-solving scenarios. ultimately employed UAV path planning, tested across range terrain obstacle models. findings confirm excels searching paths achieve safe avoidance lower trajectory costs. Its accuracy notably superior comparative algorithms, underscoring robustness efficiency. ensures balanced exploitation space, which are particularly crucial optimizing planning environments symmetrical asymmetrical constraints.

Язык: Английский

Процитировано

0
Hybrid Ensemble Architecture for Brain Tumor Segmentation Using EfficientNetB4-MobileNetV3 with Multi-Path Decoders DOI
Suhaila Abuowaida, Yazan Alnsour, Zaher Salah

и другие.

Data & Metadata, Год журнала: 2025, Номер 4, С. 374 - 374

Опубликована: Фев. 26, 2025

Brain tumor segmentation based on multi-modal magnetic resonance imaging is a challenging medical problem due to tumors heterogeneity, irregular boundaries, and inconsistent appearances. For this purpose, we propose hybrid primal dual ensemble architecture leveraging EfficientNetB4 MobileNetV3 through cross-network novel feature interaction mechanism an adaptive learning approach. The proposed method enables by recent attention mechanisms, dedicated decoders, uncertainty estimation techniques. model was extensively evaluated using the BraTS2019-2021 datasets, achieving outstanding performance with mean Dice scores of 0.91, 0.87, 0.83 whole tumor, core enhancing regions respectively. achieves stable over range types sizes, low relative computational cost.

Язык: Английский

Процитировано

0
Genomic Structural Equation Modeling Elucidates the Shared Genetic Architecture of Allergic Disorders DOI Creative Commons

J Ibáñez Ruán,

Xinglin Yi

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Abstract Background The intricate shared genetic architecture underlying allergic disorders—including asthma, atopic dermatitis, contact rhinitis, conjunctivitis, urticaria, anaphylaxis, and eosinophilic esophagitis—remains incompletely characterized. Methods Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing of disorders. Coupled with diverse post-GWAS analytical methods, we aimed discover susceptible loci investigate associations external traits. Furthermore, explored enriched pathways, cellular layers, elements, investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted assess chromosomal-level for Results A well-fitted SEM revealing We identified a total 2038 genome-wide significant SNP (p < 5e-8), including 31 previously unreported loci. Fine-mapping variants gene sets pinpointed 2 causal candidate genes. Genetic correlation analyses further illuminated multiple traits, notably psychiatric Preliminary findings four Conclusion Notably, this presents first comprehensive characterization disorders through analysis an unmeasured composite phenotype, providing novel insights into etiological pathways across these conditions.

Язык: Английский

Процитировано

0
Improved Black-Winged Kite Algorithm with Multi-Strategy Optimization for Identifying Dendrobium huoshanense DOI Creative Commons
Chaochuan Jia,

Ting Yang,

Maosheng Fu

и другие.

Biomimetics, Год журнала: 2025, Номер 10(4), С. 226 - 226

Опубликована: Апрель 4, 2025

An improved black-winged kite algorithm with multiple strategies (BKAIM) is proposed in this paper to address two critical limitations the original optimization (BKA): restricted search capability caused by low-quality initial population and reduced diversity resulting from blind following behavior during migration phase. Our enhancement implements three strategic modifications across different stages. During initialization, an opposition-based learning strategy was incorporated generate a higher-quality population. For phase, differential mutation integrated facilitate information exchange among members, mitigate tendency of leader-following behavior, enhance convergence precision, achieve optimal balance between exploration exploitation capabilities. Regarding boundary handling, conventional absorption method replaced random approach increase subsequently improve algorithm’s Comprehensive testing conducted on four benchmark function sets (CEC2017, CEC2019, CEC2021, CEC2022) validate effectiveness algorithm. Detailed analysis Wilcoxon rank-sum test comparisons other algorithms demonstrated BKAIM’s superior performance robustness. Furthermore, support vector machine (SVM) model optimized BKAIM for grade identification Dendrobium huoshanense based near-infrared spectral data, thereby confirming its practical applications.

Язык: Английский

Процитировано

0