A Comprehensive Survey on Arithmetic Optimization Algorithm

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(5), С. 3379 - 3404

Опубликована: Март 15, 2023

Язык: Английский

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An improved multi-strategy beluga whale optimization for global optimization problems

Mathematical Biosciences & Engineering, Год журнала: 2023, Номер 20(7), С. 13267 - 13317

Опубликована: Янв. 1, 2023

<abstract> <p>This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global problems and engineering problems. This improvement proposed the imbalance between exploration exploitation problem of insufficient convergence accuracy speed (BWO). In IBWO, we use a new group action strategy (GAS), replaces phase in BWO. It was inspired by hunting behavior whales nature. The GAS keeps individual belugas together, allowing them hide together from threat posed their natural enemy, tiger shark. also enables exchange location information enhance balance local lookups. On this basis, dynamic pinhole imaging (DPIS) quadratic interpolation (QIS) are added improve ability search rate IBWO maintain diversity. comparison experiment, performance algorithm tested using CEC2017 CEC2020 benchmark functions different dimensions. Performance analyzed observing experimental data, curves, box graphs, results were Wilcoxon rank sum test. show that has good robustness. Finally, applicability practical verified five problems.</p> </abstract>

Язык: Английский

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WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

IEEE Access, Год журнала: 2023, Номер 11, С. 91861 - 91878

Опубликована: Янв. 1, 2023

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters arbitrary shapes in noisy datasets. However, DBSCAN requires two input parameters: the neighborhood distance value (Eps) and minimum number sample points its (MinPts), to perform on dataset. The quality highly sensitive these parameters. To tackle this issue, paper introduces parameter-adaptive algorithm based Whale Optimization Algorithm (WOA-DBSCAN). determines parameter range dataset distribution utilizes silhouette coefficient as objective function. It iteratively selects parameters within using WOA. This approach ultimately achieves adaptive DBSCAN. Experimental results five typical artificial datasets six real UCI demonstrate effectiveness proposed WOA-DBSCAN algorithm. Compared related optimization algorithms, shows significant improvements. F-values increased by 9.8%, 13.2%, 2% respectively two-dimensional Additionally, accuracy values low medium dimensional 22.3%, 10%, 23.3%. Hence, maintain ability while achieving clustering.

Язык: Английский

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An interpretable clustering approach to safety climate analysis: Examining driver group distinctions

Accident Analysis & Prevention, Год журнала: 2023, Номер 196, С. 107420 - 107420

Опубликована: Дек. 30, 2023

Язык: Английский

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Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e2016 - e2016

Опубликована: Май 14, 2024

Equipment downtime resulting from maintenance in various sectors around the globe has become a major concern. The effectiveness of conventional reactive methods addressing interruptions and enhancing operational efficiency inadequate. Therefore, acknowledging constraints associated with growing need for proactive approaches to proactively detect possible breakdowns is necessary. optimisation asset management reduction costly emerges demand industries. work highlights use Internet Things (IoT)-enabled Predictive Maintenance (PdM) as revolutionary strategy across many sectors. This article presents picture future which IoT technology sophisticated analytics will enable prediction mitigation probable equipment failures. literature study great importance it thoroughly explores complex steps techniques necessary development implementation efficient PdM solutions. offers useful insights into enhancement by analysing current information approaches. outlines essential stages application PdM, encompassing underlying design factors, data preparation, feature selection, decision modelling. Additionally, discusses range ML models methodologies monitoring conditions. In order enhance plans, prioritise ongoing improvement field PdM. potential boosting skills guaranteeing competitiveness companies global economy significant through incorporation IoT, Artificial Intelligence (AI), advanced analytics.

Язык: Английский

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A versatile Multi-space DBSCAN framework for rough surface object segmentation

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Май 3, 2025

Язык: Английский

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An Improved Density-Based Spatial Clustering of Applications with Noise Algorithm with an Adaptive Parameter Based on the Sparrow Search Algorithm

Algorithms, Год журнала: 2025, Номер 18(5), С. 273 - 273

Опубликована: Май 6, 2025

The density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the result this algorithm exceptionally sensitive neighborhood radius (Eps) and points, it hard obtain best quickly accurately it. To address issue, a parameter-adaptive DBSCAN based on Sparrow Search Algorithm (SSA), referred as SSA-DBSCAN, proposed. This method leverages local fast search ability SSA, using optimal number clusters silhouette coefficient dataset objective functions iteratively optimize select two input parameters DBSCAN. avoids adverse impact manually inputting parameters, enabling adaptive Experiments typical synthetic datasets, UCI (University California, Irvine) real-world image segmentation tasks have validated effectiveness SSA-DBSCAN algorithm. Comparative analysis other related optimization algorithms demonstrates performance SSA-DBSCAN.

Язык: Английский

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Awareness, perceived importance, and implementation of circular economy principles: Insights from Turkish construction sector

Journal of Environmental Management, Год журнала: 2025, Номер 387, С. 125834 - 125834

Опубликована: Май 19, 2025

Язык: Английский

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An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning

Mathematical Biosciences & Engineering, Год журнала: 2023, Номер 20(4), С. 6422 - 6467

Опубликована: Янв. 1, 2023

The aquila optimization algorithm (AO) is an efficient swarm intelligence proposed recently. However, considering that AO has better performance and slower late convergence speed in the process. For solving this effect of improving its performance, paper proposes enhanced with a velocity-aided global search mechanism adaptive opposition-based learning (VAIAO) which based on simplified Aquila (IAO). In VAIAO, velocity acceleration terms are set included update formula. Furthermore, strategy introduced to improve local optima. To verify 27 classical benchmark functions, Wilcoxon statistical sign-rank experiment, Friedman test five engineering problems tested. results experiment show VAIAO than AO, IAO other comparison algorithms. This also means introduction these two strategies enhances exploration ability algorithm.

Язык: Английский

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An intelligent clustering framework for substitute recommendation and player selection

The Journal of Supercomputing, Год журнала: 2023, Номер 79(15), С. 16409 - 16441

Опубликована: Апрель 27, 2023

Язык: Английский

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