Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108803 - 108803

Published: July 1, 2024

Language: Английский

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Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 168, P. 107769 - 107769

Published: Nov. 28, 2023

Language: Английский

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Salp swarm algorithm with iterative mapping and local escaping for multi-level threshold image segmentation: a skin cancer dermoscopic case study

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(2), P. 655 - 693

Published: Jan. 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.

Language: Английский

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A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Journal of Artificial Intelligence and Soft Computing Research, Journal Year: 2024, Volume and Issue: 14(3), P. 207 - 235

Published: June 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.

Language: Английский

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Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(4), P. 1363 - 1389

Published: June 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.

Language: Английский

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An Improved Spider-Wasp Optimizer for Obstacle Avoidance Path Planning in Mobile Robots

Mathematics, Journal Year: 2024, Volume and Issue: 12(17), P. 2604 - 2604

Published: Aug. 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.

Language: Английский

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Comprehensive Technical Review of Recent Bio-Inspired Population-Based Optimization (BPO) Algorithms for Mobile Robot Path Planning

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 20942 - 20961

Published: Jan. 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

Language: Английский

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MSGJO: a new multi-strategy AI algorithm for the mobile robot path planning

The International Journal of Advanced Manufacturing Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Language: Английский

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Dynamic obstacle avoidance planning for multi-robot suspension system based on SDBO–IDWA algorithm and force–position cooperative optimization

Journal of Computational Design and Engineering, Journal Year: 2025, Volume and Issue: 12(4), P. 55 - 76

Published: March 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.

Language: Английский

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Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

Journal of Robotics, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 16

Published: May 18, 2023

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid method based on particle swarm optimization (PSO) and window approach (DWA) is proposed this paper. First, an improved (IPSO) enhance the exploration capability search accuracy of algorithm by improving velocity update inertia weight. Secondly, initialization strategy used increase population diversity, addressing local optimum make overcome optimum. Thirdly, selecting navigation points guide planning. The robot selects appropriate as target for position risk collision with obstacles. Finally, (IDWA) combining obstacle (VO) DWA, evaluation function DWA trajectory tracking avoidance capabilities. simulation experimental results show that IPSO has greater accuracy; IDWA more effective avoidance; enables efficiently environments.

Language: Английский

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