INFO: An efficient optimization algorithm based on weighted mean of vectors

Expert Systems with Applications, Год журнала: 2022, Номер 195, С. 116516 - 116516

Опубликована: Янв. 15, 2022

Язык: Английский

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Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion

Knowledge-Based Systems, Год журнала: 2023, Номер 268, С. 110454 - 110454

Опубликована: Март 11, 2023

Язык: Английский

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Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems

Advances in Engineering Software, Год журнала: 2022, Номер 174, С. 103282 - 103282

Опубликована: Окт. 29, 2022

Язык: Английский

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Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 114, С. 105139 - 105139

Опубликована: Июль 6, 2022

Язык: Английский

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Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization

Knowledge-Based Systems, Год журнала: 2022, Номер 260, С. 110146 - 110146

Опубликована: Ноя. 29, 2022

Язык: Английский

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Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 403, С. 115652 - 115652

Опубликована: Ноя. 4, 2022

Язык: Английский

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B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Computers, Год журнала: 2021, Номер 10(11), С. 136 - 136

Опубликована: Окт. 25, 2021

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there redundant irrelevant features, which reduce the performance of algorithms. To tackle this challenge, metaheuristic algorithms used to select effective However, most them scalable enough from as well small ones. Therefore, paper, a binary moth-flame optimization (B-MFO) is proposed datasets. Three categories B-MFO were developed using S-shaped, V-shaped, U-shaped transfer functions convert canonical MFO continuous binary. These evaluated on seven results compared with four well-known algorithms: BPSO, bGWO, BDA, BSSA. In addition, convergence behavior comparative assessed, statistically analyzed Friedman test. The experimental demonstrate superior solving feature selection problem for different other

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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Boosting particle swarm optimization by backtracking search algorithm for optimization problems

Swarm and Evolutionary Computation, Год журнала: 2023, Номер 79, С. 101304 - 101304

Опубликована: Март 26, 2023

Язык: Английский

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American zebra optimization algorithm for global optimization problems

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Март 30, 2023

Abstract A novel bio-inspired meta-heuristic algorithm, namely the American zebra optimization algorithm (AZOA), which mimics social behaviour of zebras in wild, is proposed this study. are distinguished from other mammals by their distinct and fascinating character leadership exercise, navies baby to leave herd before maturity join a separate with no family ties. This departure encourages diversification preventing intra-family mating. Moreover, convergence assured exercise zebras, directs speed direction group. lifestyle indigenous nature main inspiration for proposing AZOA algorithm. To examine efficiency CEC-2005, CEC-2017, CEC-2019 benchmark functions considered, compared several state-of-the-art algorithms. The experimental outcomes statistical analysis reveal that capable attaining optimal solutions maximum while maintaining good balance between exploration exploitation. Furthermore, numerous real-world engineering problems have been employed demonstrate robustness AZOA. Finally, it anticipated will accomplish domineeringly forthcoming advanced CEC complex problems.

Язык: Английский

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