2022 34th Chinese Control and Decision Conference (CCDC), Год журнала: 2024, Номер unknown, С. 1490 - 1495
Опубликована: Май 25, 2024
Язык: Английский
Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108803 - 108803
Опубликована: Июль 1, 2024
Язык: Английский
Процитировано
49
Computers in Biology and Medicine, Год журнала: 2023, Номер 168, С. 107769 - 107769
Опубликована: Ноя. 28, 2023
Язык: Английский
Процитировано
20
Journal of Artificial Intelligence and Soft Computing Research, Год журнала: 2024, Номер 14(3), С. 207 - 235
Опубликована: Июнь 1, 2024
Abstract Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, tendency to fall into local tricky multimodal problems. In order address these this study proposes ensemble called hybrid moth equilibrium (HMEO), leveraging both the flame (MFO) EO. The proposed approach first integrates potential of EO then introduces capability MFO help enhance search, fine-tuning, an appropriate balance during search process. To verify algorithm, suggested HMEO applied on 29 test functions CEC 2017 benchmark suite. results developed method are compared with several well-known metaheuristics, including basic EO, MFO, some popular variants. Friedman rank employed measure newly statistically. Moreover, introduced has been mobile robot path planning (MRPP) problem investigate its problem-solving ability real-world experimental show reported comparative approaches.
Язык: Английский
Процитировано
8
Journal of Computational Design and Engineering, Год журнала: 2023, Номер 10(2), С. 655 - 693
Опубликована: Янв. 11, 2023
Abstract If found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient reliable tool during the fore-period detection stage of cancer, so efficient processing digital images dermoscopy particularly critical to improving level cancer diagnosis. Notably, image segmentation part preprocessing essential technical support in process processing. In addition, multi-threshold (MIS) technology extensively used due its straightforward effective features. Many academics have coupled different meta-heuristic algorithms with MIS raise quality. Nonetheless, these frequently enter local optima. Therefore, this paper suggests an improved salp swarm algorithm (ILSSA) method that combines iterative mapping escaping operator address drawback. Besides, also proposes ILSSA-based approach, which triumphantly utilized segment dermoscopic cancer. This uses two-dimensional (2D) Kapur’s entropy as objective function employs non-local means 2D histogram represent information. Furthermore, array benchmark test experiments demonstrated ILSSA could alleviate optimal problem more effectively than other compared algorithms. Afterward, experiment displayed proposed obtained superior results peers was adaptable at thresholds.
Язык: Английский
Процитировано
17
Journal of Computational Design and Engineering, Год журнала: 2023, Номер 10(4), С. 1363 - 1389
Опубликована: Июнь 13, 2023
Abstract The paper addresses the limitations of Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. MFO which employs moths' transverse orientation navigation technique, has been generate solutions for such However, performance is dependent on flame production and spiral search components, mechanism could still be improved concerning diversity flames ability find solutions. authors propose revised version called GMSMFO, uses Novel Gaussian mutation shrink enhance population balance exploration exploitation capabilities. study evaluates GMSMFO using CEC 2017 benchmark 20 datasets, including high-dimensional intrusion detection system dataset. proposed algorithm compared other advanced metaheuristics, its evaluated statistical tests as Friedman Wilcoxon rank-sum. shows that highly competitive frequently superior algorithms. It can identify ideal feature subset, improving classification accuracy reducing number features used. main contribution this research includes improvement exploration/exploitation expansion local search. ranging controller diversity. compares with traditional metaheuristic algorithms 29 benchmarks application binary selection benchmarks, systems. (Wilcoxon rank-sum Friedman) evaluate source code available at https://github.com/MohammedQaraad/GMSMFO-algorithm.
Язык: Английский
Процитировано
13
Mathematics, Год журнала: 2024, Номер 12(17), С. 2604 - 2604
Опубликована: Авг. 23, 2024
The widespread application of mobile robots holds significant importance for advancing social intelligence. However, as the complexity environment increases, existing Obstacle Avoidance Path Planning (OAPP) methods tend to fall into local optimal paths, compromising reliability and practicality. Therefore, based on Spider-Wasp Optimizer (SWO), this paper proposes an improved OAPP method called LMBSWO address these challenges. Firstly, learning strategy is introduced enhance diversity algorithm population, thereby improving its global optimization performance. Secondly, dual-median-point guidance incorporated algorithm’s exploitation capability increase path searchability. Lastly, a better ability escape paths. Subsequently, employed in five different map environments. experimental results show that achieves advantage collision-free length, with 100% probability, across maps complexity, while obtaining 80% fault tolerance maps, compared nine novel efficient ranks first trade-off between planning time length. With results, can be considered robust solving performance, along high robustness.
Язык: Английский
Процитировано
4
IEEE Access, Год журнала: 2024, Номер 12, С. 20942 - 20961
Опубликована: Янв. 1, 2024
Over recent decades, the field of mobile robot path planning has evolved significantly, driven by pursuit enhanced navigation solutions. The need to determine optimal trajectories within complex environments led exploration diverse methodologies. This paper focuses on a specific subset: Bio-inspired Population-based Optimization (BPO) BPO methods play pivotal role in generating efficient paths for planning. Amidst abundance optimization approaches over past decade, only fraction studies have effectively integrated these into strategies. focus is years 2014-2023, reviewing techniques applied challenges. Contributions include comprehensive review planning, along with an experimental methodology method comparison under consistent conditions. encompasses same environment, initial conditions, and replicates. A multi-objective function incorporated evaluate methods. delves key concepts, mathematical models, algorithm implementations examined techniques. setup, methodology, benchmarking performance results are discussed. In conclusion, this reviews algorithms introduces standardized approach algorithms, providing insights their strengths challenges
Язык: Английский
Процитировано
3
CAAI Transactions on Intelligence Technology, Год журнала: 2024, Номер unknown
Опубликована: Апрель 1, 2024
Abstract The Equilibrium Optimiser (EO) has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems. Balancing paradox between exploration and exploitation operations while enhancing ability jump out local optimum are two key points addressed in EO research. To alleviate these limitations, an variant named adaptive elite‐guided (AEEO) is introduced. Specifically, search mechanism enhances balance exploitation. modified mutualism phase reinforces information interaction among particles optima avoidance. cooperation mechanisms boosts overall performance basic EO. AEEO subjected competitive experiments with state‐of‐the‐art on 23 classical benchmark functions IEE CEC 2017 function test suite. Experimental results demonstrate outperforms several well‐performing variants, DE PSO SSA GWO variants terms convergence speed accuracy. In addition, algorithm used for edge server (ES) placement problem mobile computing (MEC) environments. experimental show author’s approach representative approaches compared access latency deployment cost.
Язык: Английский
Процитировано
3
The International Journal of Advanced Manufacturing Technology, Год журнала: 2025, Номер unknown
Опубликована: Апрель 2, 2025
Язык: Английский
Процитировано
0
Journal of Computational Design and Engineering, Год журнала: 2025, Номер 12(4), С. 55 - 76
Опубликована: Март 25, 2025
Abstract To address dynamic obstacle avoidance planning in multi-robot coordinated suspension systems (MCSS), this study proposes a hybrid method integrating an enhanced stable dung beetle optimization (SDBO) algorithm with improved window approach (IDWA). Dynamic obstacles are addressed through IDWA-based trajectory prediction, while the SDBO–IDWA optimizes trajectories for suspended objects. Furthermore, leveraging force–position cooperative optimization, resolves coupled kinematic and constraints inherent MCSS. Simulation experimental results demonstrate that outperforms traditional approaches, achieving 19.95% reduction minimum length 57.77% decrease runtime For towing robots, it reduces optimal by 9.52% fitness values 9.44%. The findings advance theory enable safe, diverse applications.
Язык: Английский
Процитировано
0