Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

Mathematics and Computers in Simulation, Год журнала: 2022, Номер 202, С. 343 - 373

Опубликована: Июнь 17, 2022

Язык: Английский

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DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

Advanced Engineering Informatics, Год журнала: 2023, Номер 57, С. 102004 - 102004

Опубликована: Июнь 8, 2023

Язык: Английский

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Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122200 - 122200

Опубликована: Окт. 23, 2023

Язык: Английский

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A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems

Knowledge-Based Systems, Год журнала: 2023, Номер 271, С. 110554 - 110554

Опубликована: Апрель 10, 2023

Язык: Английский

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Quadratic Interpolation Optimization (QIO): A new optimization algorithm based on generalized quadratic interpolation and its applications to real-world engineering problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 417, С. 116446 - 116446

Опубликована: Сен. 28, 2023

Язык: Английский

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SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning

Expert Systems with Applications, Год журнала: 2023, Номер 223, С. 119941 - 119941

Опубликована: Март 23, 2023

Язык: Английский

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MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications

Advanced Engineering Informatics, Год журнала: 2024, Номер 61, С. 102464 - 102464

Опубликована: Март 15, 2024

Язык: Английский

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Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Янв. 2, 2024

Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal multimodal problems. However, convergence speed performance still have some deficiencies when complex multidimensional Therefore, this paper proposes hybrid method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive spiral predation strategy, Nelder-Mead simplex search (NM). Firstly, initialization phase, QOBL strategy introduced. This reconstructs initial spatial position population by pairwise comparisons to obtain more prosperous higher quality population. Subsequently, an designed exploration exploitation phases. The first learns optimal individual positions dimensions through avoid loss local optimality. At same time, movement motivated cosine factor introduced maintain balance between exploitation. Finally, NM added. It corrects multiple scaling methods improve accurately efficiently. verified utilizing CEC2017 CEC2019 test functions. Meanwhile, superiority six engineering design examples. experimental results show has feasibility effectiveness practical problems than methods.

Язык: Английский

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A Halton Enhanced Solution-based Human Evolutionary Algorithm for Complex Optimization and Advanced Feature Selection Problems

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113062 - 113062

Опубликована: Янв. 1, 2025

Язык: Английский

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MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 403, С. 115676 - 115676

Опубликована: Окт. 27, 2022

Язык: Английский

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