Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning

Energy, Год журнала: 2024, Номер 292, С. 130505 - 130505

Опубликована: Янв. 29, 2024

Язык: Английский

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Optimization of a wind farm layout to mitigate the wind power intermittency

Applied Energy, Год журнала: 2024, Номер 367, С. 123383 - 123383

Опубликована: Май 10, 2024

Язык: Английский

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(6)

Опубликована: Авг. 18, 2024

Abstract This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects QLMA, including parameter adaptation, operator selection, and balancing global exploration local exploitation. QLMA has become a leading solution industries like energy, power systems, engineering, addressing range mathematical challenges. Looking forward, we suggest further integration, transfer learning strategies, techniques to reduce state space. article is categorized under: Technologies > Computational Intelligence Artificial

Язык: Английский

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Reinforcement learning for mutation operator selection in automated program repair

Automated Software Engineering, Год журнала: 2025, Номер 32(2)

Опубликована: Март 15, 2025

Abstract Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based repair, a search space mutated variants is explored find potential patches for Most commonly, every selection mutation operator during performed uniformly at random, which can generate many buggy, even uncompilable programs. Our goal reduce generation that do not compile or break intended functionality waste considerable resources. this paper, we investigate feasibility reinforcement learning-based approach operators in repair. proposed programming language, granularity-level, and strategy agnostic allows easy augmentation into existing tools. We conducted an extensive empirical evaluation four techniques, two reward types, credit assignment strategies, integration methods, three sets using 30,080 independent attempts. evaluated our on 353 real-world bugs from Defects4J benchmark. The results higher number test-passing variants, but does exhibit noticeable improvement patched comparison baseline, uniform random selection. While learning has been previously shown be successful improving evolutionary algorithms, often used it yet demonstrate such improvements when applied area research.

Язык: Английский

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Optimal control of HVAC systems through active disturbance rejection control-assisted reinforcement learning in energy-aware buildings

Energy, Год журнала: 2025, Номер unknown, С. 135824 - 135824

Опубликована: Март 1, 2025

Язык: Английский

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An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

Journal of Bionic Engineering, Год журнала: 2024, Номер 21(3), С. 1522 - 1540

Опубликована: Апрель 3, 2024

Язык: Английский

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Differential evolution with multi-strategies for UAV trajectory planning and point cloud registration

Applied Soft Computing, Год журнала: 2024, Номер 167, С. 112466 - 112466

Опубликована: Ноя. 13, 2024

Язык: Английский

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Reinforcement learning-based particle swarm optimization for wind farm layout problems

Energy, Год журнала: 2024, Номер unknown, С. 134050 - 134050

Опубликована: Дек. 1, 2024

Язык: Английский

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A novel binary genetic differential evolution optimization algorithm for wind layout problems

AIMS energy, Год журнала: 2024, Номер 12(1), С. 321 - 349

Опубликована: Янв. 1, 2024

<abstract><p>This paper addresses the increasingly critical issue of environmental optimization in context rapid economic development, with a focus on wind farm layout optimization. As demand for sustainable resource management, climate change mitigation, and biodiversity conservation rises, so does complexity managing impacts promoting practices. Wind optimization, vital subset involves strategic placement turbines to maximize energy production minimize impacts. Traditional methods, such as heuristic approaches, gradient-based rule-based strategies, have been employed tackle these challenges. However, they often face limitations exploring solution space efficiently avoiding local optima. To advance field, this study introduces LSHADE-SPAGA, novel algorithm that combines binary genetic operator LSHADE differential evolution algorithm, effectively balancing global exploration exploitation capabilities. This hybrid approach is designed navigate complexities considering factors like patterns, terrain, land use constraints. Extensive testing, including 156 instances across different scenarios constraints, demonstrates LSHADE-SPAGA's superiority over seven state-of-the-art algorithms both ability jumping out optima quality.</p></abstract>

Язык: Английский

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A Novel Integrated Optimization Method of Micrositing and Cable Routing for Offshore Wind Farms

Energy, Год журнала: 2024, Номер 306, С. 132443 - 132443

Опубликована: Июль 17, 2024

Язык: Английский

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