Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 30, 2025

Abstract The classification of chronic diseases has long been a prominent research focus in the field public health, with widespread application machine learning algorithms. Diabetes is one high prevalence worldwide and considered disease its own right. Given nature this condition, numerous researchers are striving to develop robust algorithms for accurate classification. This study introduces revolutionary approach accurately classifying diabetes, aiming provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) proposed combination Kernel Extreme Learning Machine (KELM) diabetes prediction model. First, (SBOA) enhanced by integrating particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, quantum computing-based t-distribution variations. performance QHSBOA validated using CEC2017 benchmark suite. Subsequently, used optimize kernel penalty parameter $$\:C$$ bandwidth $$\:c$$ KELM. Comparative experiments other models conducted datasets. experimental results indicate that QHSBOA-KELM model outperforms comparative four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, specificity. offers an effective method early diagnosis diabetes.

Язык: Английский

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Two-Layer Optimal Scheduling Model of Microgrid Considering Demand Response Based on Improved Nutcracker Optimization Algorithm

Processes, Год журнала: 2025, Номер 13(2), С. 585 - 585

Опубликована: Фев. 19, 2025

To comprehensively address the interests of both supply and demand sides within a microgrid, two-layer optimal scheduling model incorporating response was formulated. The upper tier aims to optimize load profile, focusing on maximizing electricity consumption satisfaction minimizing user costs. Meanwhile, lower targets optimization output from each controllable generation unit, with goal reducing operational Given nonlinear multi-constrained nature this model, an improved nutcracker algorithm (INOA) is proposed. This enhancement introduces chaotic sequences into original (NOA) for population initialization, employs hybrid butterfly enhance algorithm’s local search capabilities, integrates dynamic selection adaptive T-distribution updating individual positions. solution tests involving INOA, NOA, dung beetle optimizer (DOB), particle swarm (PSO), grey wolf (GWO), sparrow (SSA) were conducted using CEC2022 intelligent test suite. Analysis reveals that INOA exhibits superior comprehensive performance compared other algorithms, validating effectiveness improvements introduced in paper. Ultimately, simulation analysis microgrid performed, demonstrating that, despite 3.58% reduction satisfaction, participation led 25.16% decrease costs 5.92% These findings substantiate model’s capability effectively balance economic microgrid.

Язык: Английский

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Advancing Engineering Solutions with Protozoa-Based Differential Evolution: A Hybrid Optimization Approach

Automation, Год журнала: 2025, Номер 6(2), С. 13 - 13

Опубликована: Март 28, 2025

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of (APO) powerful optimization strategies (DE) to address complex and engineering design challenges. The HPDE algorithm is designed balance exploration exploitation features, utilizing innovative features such as autotrophic heterotrophic foraging behaviors, dormancy, reproduction processes alongside DE strategy. performance was evaluated on CEC2014 benchmark functions, it compared against two sets state-of-the-art optimizers comprising 23 different algorithms. results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out 30 from first set second set. Additionally, has been successfully applied range problems, including robot gripper optimization, welded beam pressure vessel spring speed reducer cantilever three-bar truss optimization. consistently showcase solving these problems when competing

Язык: Английский

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Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 23, 2025

The Gazelle Optimization Algorithm (GOA) is a recently proposed and widely recognized metaheuristic algorithm. However, it suffers from slow convergence, low precision, tendency to fall into local optima when addressing practical problems. To address these limitations, we propose Multi-Strategy Improved (MIGOA). Key enhancements include population initialization based on an optimal point set, tangent flight search strategy, adaptive step size factor, novel exploration strategies. These improvements collectively enhance GOA's capability, convergence speed, effectively preventing becoming trapped in optima. We evaluated MIGOA using the CEC2017 CEC2020 benchmark test sets, comparing with GOA eight other algorithms. results, validated by Wilcoxon rank-sum Friedman mean rank test, demonstrate that achieves average rankings of 1.80, 2.03, 2.70 (Dim = 30/50/100) 20), respectively, outperforming standard high-performance optimizers. Furthermore, application three-dimensional unmanned aerial vehicle (UAV) path planning problems 2 engineering optimization design further validates its potential solving constrained Experimental results consistently indicate exhibits strong scalability applicability.

Язык: Английский

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An innovative hybrid method combining grey wolf and marine predator optimization techniques for global and constrained problem-solving

The Journal of Supercomputing, Год журнала: 2025, Номер 81(8)

Опубликована: Май 29, 2025

Язык: Английский

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