Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130603 - 130603
Published: May 1, 2025
Language: Английский
Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102958 - 102958
Published: Sept. 1, 2024
Language: Английский
Citations
17
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: March 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.
Language: Английский
Citations
2
Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 120, P. 116381 - 116381
Published: April 9, 2025
Language: Английский
Citations
2
Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 323, P. 119231 - 119231
Published: Nov. 11, 2024
Language: Английский
Citations
7
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 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
Language: Английский
Citations
1
Expert Systems, Journal Year: 2025, Volume and Issue: 42(4)
Published: March 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.
Language: Английский
Citations
1
The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)
Published: Jan. 6, 2025
Language: Английский
Citations
0
Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107457 - 107457
Published: Jan. 8, 2025
Language: Английский
Citations
0
Journal of Computational Design and Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 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.
Language: Английский
Citations
0
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 30, 2025
Language: Английский
Citations
0