MORIME: A Multi-Objective RIME Optimization Framework for Efficient Truss Design

Results in Engineering, Год журнала: 2025, Номер 25, С. 103933 - 103933

Опубликована: Янв. 5, 2025

Язык: Английский

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Optimization of truss structures with two archive-boosted MOHO algorithm

Alexandria Engineering Journal, Год журнала: 2025, Номер 120, С. 296 - 317

Опубликована: Фев. 18, 2025

Язык: Английский

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An enhanced ivy algorithm fusing multiple strategies for global optimization problems

Advances in Engineering Software, Год журнала: 2025, Номер 203, С. 103862 - 103862

Опубликована: Фев. 6, 2025

Язык: Английский

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An efficient enhanced exponential distribution optimizer: applications in global, engineering, and combinatorial optimization problems

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 13, 2025

Язык: Английский

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Artificial Satellite Search: A New Metaheuristic Algorithm for Optimizing Truss Structure Design and Project Scheduling

Applied Mathematical Modelling, Год журнала: 2025, Номер unknown, С. 116008 - 116008

Опубликована: Фев. 1, 2025

Язык: Английский

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Efficiency Analysis of Binary Metaheuristic Optimization Algorithms for Uncapacitated Facility Location Problems

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112968 - 112968

Опубликована: Март 1, 2025

Язык: Английский

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UAV Path Planning: A Dual-Population Cooperative Honey Badger Algorithm for Staged Fusion of Multiple Differential Evolutionary Strategies

Biomimetics, Год журнала: 2025, Номер 10(3), С. 168 - 168

Опубликована: Март 10, 2025

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources UAV constraints is constructed to reflect actual environment. Second, LRMHBA improves global search by optimizing initial population distribution through integration Latin hypercube sampling elite strategy. Subsequently, stochastic perturbation mechanism introduced facilitate escape from local optima. Furthermore, adapt evolving exploration requirements during process, employs differential mutation strategy tailored populations with different fitness values, utilizing individuals initialization stage guide process. This design forms two-population cooperative that enhances balance between exploitation, thereby improving accuracy. Experimental evaluations on CEC2017 benchmark suite demonstrate superiority over 11 comparison algorithms. In task, consistently generated shortest average across three obstacle simulation scenarios varying complexity, achieving highest rank Friedman test.

Язык: Английский

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Machine Learning Approaches for Predicting Mechanical Properties of Steel-Fiber-Reinforced Concrete

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112149 - 112149

Опубликована: Март 1, 2025

Язык: Английский

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Hyperspectral estimation of soil organic matter using improved spotted hyena optimizer and iteratively retained informative variables

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 113410 - 113410

Опубликована: Март 1, 2025

Язык: Английский

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mESC: An Enhanced Escape Algorithm Fusing Multiple Strategies for Engineering Optimization

Biomimetics, Год журнала: 2025, Номер 10(4), С. 232 - 232

Опубликована: Апрель 8, 2025

A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address challenges balancing exploration and development stages low convergence accuracy in (ESC). Firstly, an adaptive perturbation factor strategy was employed maintain population diversity. Secondly, introducing a restart mechanism enhance capability mESC. Finally, dynamic centroid reverse learning designed balance local development. In addition, order accelerate global speed, boundary adjustment based on elite pool proposed, which selects individuals replace bad individuals. Comparing mESC with latest metaheuristic high-performance winner CEC2022 testing suite, numerical results confirmed that outperforms other competitors. superiority handling problems verified through several classic real-world optimization problems.

Язык: Английский

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