MSGGSA: a multi-strategy-guided gravitational search algorithm for gene selection in cancer classification

Pattern Analysis and Applications, Год журнала: 2025, Номер 28(2)

Опубликована: Март 14, 2025

Язык: Английский

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A multi-strategy improved hunger games search algorithm

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 30, 2025

This paper proposes a Multi-strategy Improved Hunger Games Search (MHGS) algorithm to address the inherent limitations of original HGS algorithm, including imbalanced exploration-exploitation capabilities, insufficient population diversity, and premature convergence. The main contributions feature four synergistic innovation mechanisms: (1) A phased position update framework dynamically coordinates global exploration local exploitation through three distinct search phases; (2) An enhanced reproduction operator mimics biological reproductive patterns maintain diversity; (3) adaptive boundary handling system redirects out-of-bounds individuals promising regions, improving efficiency; (4) elite dynamic oppositional learning strategy with self-adjusting coefficients enhances optima avoidance. proposed mechanisms demonstrate effects: macro/micro-search patterns, while jointly solution complemented by learning's perturbation effects. Extensive evaluations on 23 benchmark functions, CEC2017 test suite, two engineering designs reveal MHGS's superior performance, achieving 23.7% average accuracy improvement over seven state-of-the-art algorithms (Wilcoxon rank-sum p < 0.05). Furthermore, binary variant BMHGS_V3 sigmoid transformation attains 92.3% classification ten UCI datasets for selection. establishes novel complex optimization, demonstrating both theoretical significance practical value in computational intelligence.

Язык: Английский

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Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer

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

Опубликована: Май 2, 2025

Magnetic target state estimation is a widely applied technology, but it also faces many challenges in practical applications. One of the most critical issue accuracy. The Grey Wolf Optimizer (GWO) one more successful swarm intelligence algorithms recent years, its shortcomings have been exposed when facing increasingly complex problems. Therefore, Multi-Strategy Improved (MSIGWO) algorithm has proposed to enhance accuracy magnetic estimation. In initialization phase, Tent chaos mapping introduced population diversity, prevent falling into local optima, and improve convergence speed. Multi-population fusion evolution strategies accuracy, global search ability. Nonlinear factors better balance exploration exploitation behaviors. Dynamic weight increase diversity samples reduce likelihood optima. Adaptive dimensional learning balances searches, enhancing diversity. Levy flight enhances ability jump out optima ensures CEC2018 benchmark function set 29 problems problems, MSIGWO was tested, statistical indicators Friedman test results show that compared with GWO advanced variants, superior performance. application this proven effectiveness applicability.

Язык: Английский

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A novel meta-heuristic optimization algorithm inspired by water uptake and transport in plants

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Май 2, 2025

Язык: Английский

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Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(5), С. 873 - 873

Опубликована: Апрель 27, 2025

The safe and efficient design of dynamic submarine cables is critical for the reliability floating offshore wind turbines, yet traditional time-domain simulation-based optimization approaches are computationally intensive time consuming. To address this challenge, study proposes a closed-loop framework that couples machine learning with intelligent algorithms cable configuration design. A high-fidelity surrogate model based on backpropagation (BP) neural network was trained to accurately predict responses. Three algorithms—Particle Swarm Optimization (PSO), Ivy (IVY), Tornado (TOC)—were evaluated their effectiveness in optimizing arrangement buoyancy weight blocks. TOC algorithm exhibited superior accuracy convergence stability. results show an 18.3% reduction maximum curvature while maintaining allowable effective tension limits. This approach significantly enhances efficiency provides viable strategy systems. Future work will incorporate platform motions induced by turbine operation explore multi-objective schemes further improve performance.

Язык: Английский

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A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

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