Forecasting and early warning of shield tunnelling-induced ground collapse in rock-soil interface mixed ground using multivariate data fusion and Catastrophe Theory

Engineering Geology, Год журнала: 2024, Номер 335, С. 107548 - 107548

Опубликована: Май 10, 2024

Язык: Английский

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An improved population segmentation-based multi-mutation differential evolution algorithm for parameter extraction of photovoltaic models

Energy Conversion and Management, Год журнала: 2025, Номер 327, С. 119553 - 119553

Опубликована: Янв. 28, 2025

Язык: Английский

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Improvement of differential evolution variants with nonlinear population adjustment and parameter adaption

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126945 - 126945

Опубликована: Фев. 1, 2025

Язык: Английский

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Rolling discrete grey periodic power model with interaction effect under dual processing and its application

Expert Systems with Applications, Год журнала: 2024, Номер 254, С. 124487 - 124487

Опубликована: Июнь 13, 2024

Язык: Английский

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An improved reinforcement learning-based differential evolution algorithm for combined economic and emission dispatch problems

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 140, С. 109709 - 109709

Опубликована: Ноя. 29, 2024

Язык: Английский

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Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Mathematics, Год журнала: 2024, Номер 12(14), С. 2283 - 2283

Опубликована: Июль 22, 2024

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, accessibility while minimizing total travel distance. H-RL-VaNSAS evaluated against leading methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), Salp Swarm (SSA). Using metrics such as Hypervolume Average Ratio of Pareto Optimal Solutions, demonstrates superior performance. Specifically, achieved highest resilience index (550), sustainability (370), score (480), preferences (300), (2300), distance 950 km. Compared other improved by 12.24–17.02%, 5.71–12.12%, 4.35–9.09%, 7.14–13.21%, 4.55–9.52%, reduced 9.52–17.39%. research offers framework designing efficient, public transit that align with planning goals. The integration reinforcement learning VaNSAS significantly enhances capabilities, providing valuable tool communities.

Язык: Английский

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Task-Driven Virtual Machine Optimization Placement Model and Algorithm

Future Internet, Год журнала: 2025, Номер 17(2), С. 73 - 73

Опубликована: Фев. 7, 2025

In cloud data centers, determining how to balance the interests of user and service provider is a challenging issue. this study, task-loading-oriented virtual machine (VM) optimization placement model algorithm proposed integrating consideration both VM user’s computing requirements. First, modeled as multi-objective problem minimize makespan loading tasks, rental costs, energy consumption centers; then, an improved chaos-elite NSGA-III (CE-NSGAIII) presented by casting logistic mapping-based population initialization (LMPI) elite-guided in NSGA-III; finally, CE-NSGAIII employed solve aforementioned model, further, through combination above sub-algorithms, CE-NSGAIII-based method developed. The experiment results show that Pareto solution set obtained using exhibits better convergence diversity than those compared algorithms yields optimized scheme with shorter makespan, less lower consumption.

Язык: Английский

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A periodic intervention and strategic collaboration mechanisms based differential evolution algorithm for global optimization

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113137 - 113137

Опубликована: Апрель 1, 2025

Язык: Английский

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An adaptive differential evolution algorithm based on individual-level intervention strategy for global optimization

Expert Systems with Applications, Год журнала: 2025, Номер 286, С. 128054 - 128054

Опубликована: Май 16, 2025

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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