The Status-based Optimization: Algorithm and comprehensive performance analysis DOI
Jian Wang, Yi Chen, Chenglang Lu

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 130603 - 130603

Опубликована: Май 1, 2025

Язык: Английский

Frequency regulation of PV-reheat thermal power system via a novel hybrid educational competition optimizer with pattern search and cascaded PDN-PI controller DOI Creative Commons
Serdar Ekinci, Davut İzci, Özay Can

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102958 - 102958

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

17

Multi-strategy enterprise development optimizer for numerical optimization and constrained problems DOI Creative Commons
Xinyu Cai, Weibin Wang, Yijiang Wang

и другие.

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

Опубликована: Март 27, 2025

Abstract Enterprise Development Optimizer (EDO) is a meta-heuristic algorithm inspired by the enterprise development process with strong global search capability. However, analysis of EDO shows that it suffers from defects rapidly decreasing population diversity and weak exploitation ability when dealing complex optimization problems, while its algorithmic structure has room for further enhancement in process. In order to solve these challenges, this paper proposes multi-strategy optimizer called MSEDO based on basic EDO. A leader-based covariance learning strategy proposed, aiming strengthen quality agents alleviate later stage through guiding role dominant group modifying leader. To dynamically improve local capability algorithm, fitness distance-based leader selection proposed. addition, reconstructed diversity-based restart presented. The utilized assist jump out optimum stuck stagnation. Ablation experiments verify effectiveness strategies algorithm. performance confirmed comparing five different types improved metaheuristic algorithms. experimental results CEC2017 CEC2022 show effective escaping optimums favorable exploration capabilities. ten engineering constrained problems competently real-world problems.

Язык: Английский

Процитировано

1

Robust parameter estimation of proton exchange membrane fuel cell using Huber loss statistical function DOI

Bahaa Saad,

Ragab A. El‐Sehiemy, Hany M. Hasanien

и другие.

Energy Conversion and Management, Год журнала: 2024, Номер 323, С. 119231 - 119231

Опубликована: Ноя. 11, 2024

Язык: Английский

Процитировано

7

Smart crop disease monitoring system in IoT using optimization enabled deep residual network DOI Creative Commons
Ashish Saini, Nasib Singh Gill, Preeti Gulia

и другие.

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

Опубликована: Янв. 9, 2025

Abstract The Internet of Things (IoT) has recently attracted substantial interest because its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. current study, a new model is developed to categorize within an IoT network. network simulated monitoring crop diseases. Routing performed with Henry Gas Chicken Swarm Optimization (HGCSO), which designed by integrating Solubility (HGSO) and (CSO). fitness parameters include delay, energy, distance, link lifetime (LLT). At Base Station (BS), disease categorization collecting leaf images. Preprocessing done on input images using median filtering. Various features, such as Histogram Oriented Gradient (HoG), statistical Spider Local Image Features (SLIF), Ternary Patterns (LTP) are extracted. Plant carried out Deep Residual Network (DRN), trained Caviar (CHGCSO) that combines CAViaR HGCSO. Comparative results show accuracy 94.3%, maximum sensitivity 93.3%, specificity 92%, F1-score 93%, indicating CHGCSO-based DRN outperforms existing Graphic

Язык: Английский

Процитировано

1

Artificial Orca Optimiser: Theory and Applications for Global Optimisation Problems DOI Open Access
Lin Wang, Xuerui Wang, Yingying Pi

и другие.

Expert Systems, Год журнала: 2025, Номер 42(4)

Опубликована: Март 10, 2025

ABSTRACT With the growing complexity of real‐world engineering optimisation problems, interest in meta‐heuristic algorithms is increasing. However, existing still suffer from several shortcomings, including a poor balance between global and local search, tendency to converge toward centre solution space, susceptibility getting trapped optima. To overcome these novel algorithm, called artificial orca optimiser (AOO), proposed based on unique behaviours orcas nature. Within framework AOO, switching factor, guidance phase, iterative formulas that do not are designed enhance equilibrium exploration exploitation, ensure agents ability escape optimum, comprehensively explore space without being limited thereby increasing likelihood finding optimal solution. Qualitative, quantitative, scalability, sensitivity, practical application analyses experimental results demonstrate AOO overcomes issue converging alleviates problems exhibits outstanding optimising performance, fast convergence, great high robustness, excellent practicality.

Язык: Английский

Процитировано

1

CGWRIME: collaboration and competition-boosted RIME optimizer for engineering optimization problems DOI
Zhen Wang, Dong Zhao, Ali Asghar Heidari

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(2)

Опубликована: Янв. 6, 2025

Язык: Английский

Процитировано

0

An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation DOI
Zenglin Qiao, Lingyu Wu, Ali Asghar Heidari

и другие.

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107457 - 107457

Опубликована: Янв. 8, 2025

Язык: Английский

Процитировано

0

A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images DOI Creative Commons
Shuhui Hao, Changcheng Huang, Yi Chen

и другие.

Journal of Computational Design and Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 14, 2025

Abstract Multi-threshold image segmentation (MTIS) is a crucial technology in processing, characterized by simplicity and efficiency, the key lies selection of thresholds. However, method's time complexity will grow exponentially with number To solve this problem, an improved arithmetic optimization algorithm (ETAOA) proposed paper, optimizer for optimizing process merging appropriate Specifically, two strategies are introduced to optimize optimal threshold process: elite evolutionary strategy (EES) tracking (ETS). First, verify performance ETAOA, mechanism comparison experiments, scalability tests, experiments nine state-of-the-art peers executed based on benchmark functions CEC2014 CEC2022. After that, demonstrate feasibility ETAOA domain, were performed using ten advanced methods skin cancer dermatoscopy datasets under low high thresholds, respectively. The above experimental results show that performs outstanding compared functions. Moreover, domain has superior conditions.

Язык: Английский

Процитировано

0

Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons DOI Creative Commons

K. Jayasree,

Malaya Kumar Hota

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

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0

Discrimination of Chinese prickly ash origin place using electronic nose system and feature extraction with support vector boosting machine DOI Creative Commons
Junbo Lian, Peng Wu,

Wenhui Han

и другие.

Cogent Food & Agriculture, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 16, 2025

Язык: Английский

Процитировано

0