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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A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121723 - 121723

Опубликована: Сен. 22, 2023

Язык: Английский

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Study on a new metaheuristic algorithm – Tribal intelligent evolution optimization and its application in optimal control of cooling plants

Applied Energy, Год журнала: 2025, Номер 383, С. 125339 - 125339

Опубликована: Янв. 15, 2025

Язык: Английский

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Hyper-heuristics: A survey and taxonomy

Computers & Industrial Engineering, Год журнала: 2023, Номер 187, С. 109815 - 109815

Опубликована: Дек. 4, 2023

Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional methods, which primarily employ space-based strategies. Due the remarkable performance of hyper-heuristics in multi-objective machine learning-based optimization, there has been an increasing interest this field. With a fresh perspective, our work extends current taxonomy presents overview most significant hyper-heuristic studies last two decades. Four categories under we analyze selection (including learning techniques), low-level heuristics, target problems, parallel hyper-heuristics. Future research prospects, trends, prospective fields study also explored.

Язык: Английский

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A Robust Automatic Generation Control System based on Hybrid Aquila Optimizer-Sine Cosine Algorithm

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103951 - 103951

Опубликована: Янв. 1, 2025

Язык: Английский

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A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

Alexandria Engineering Journal, Год журнала: 2023, Номер 81, С. 469 - 488

Опубликована: Сен. 22, 2023

There are many tricky optimization problems in real life, and metaheuristic algorithms the most effective way to solve at a lower cost. The dung beetle algorithm (DBO) is more innovative proposed 2022, which affected by action of beetles such as ball rolling, foraging, reproduction. Therefore, A based on quasi-oppositional learning Q-learning (QOLDBO). First, quantum state update idea cleverly integrated into increase randomness generated population. And best behavior pattern selected adding rolling stage improve search effect. In addition, variable spiral local domain method make up for shortage developing only around neighborhood optimum. For optimal solution each iteration, dimensional adaptive Gaussian variation retained. Experimental performance tests show that QOLDBO performs well both benchmark test functions CEC 2017. Simultaneously, validity verified several classical practical application engineering problems.

Язык: Английский

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Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer

Expert Systems with Applications, Год журнала: 2023, Номер 235, С. 121212 - 121212

Опубликована: Авг. 18, 2023

Язык: Английский

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Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124765 - 124765

Опубликована: Июль 17, 2024

Язык: Английский

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Improving teaching-learning-based optimization algorithm with golden-sine and multi-population for global optimization

Mathematics and Computers in Simulation, Год журнала: 2024, Номер 221, С. 94 - 134

Опубликована: Фев. 17, 2024

Язык: Английский

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Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 434, С. 117588 - 117588

Опубликована: Ноя. 29, 2024

Язык: Английский

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