Algorithms

20 pages, 733 KiB

Open AccessArticle

Energy Optimization in Hotels: Strategies for Efficiency in Hot Water Systems

by Yarelis Valdivia Nodal, Luis Angel Iturralde Carrera, Araceli Zapatero-Gutiérrez, Mario Antonio Álvarez Guerra Plasencia, Royd Reyes Calvo, José M. Álvarez-Alvarado and Juvenal Rodríguez-Reséndiz

Algorithms 2025, 18(6), 301; https://doi.org/10.3390/a18060301 (registering DOI) - 22 May 2025

Abstract

This paper presents a procedure for the energy optimization of domestic hot water (DHW) systems in hotels located in tropical climates that use centralized air conditioning systems. The study aims to maximize heat recovery from chillers and reduce the fuel consumption of auxiliary heaters by optimizing operational variables such as water mass flow in the primary and secondary DHW circuits and outlet temperature of the backup system. The optimization is implemented using genetic algorithms (GA), which enable the identification of the most efficient flow configurations under variable thermal demand conditions. The proposed methodology integrates a thermoenergetic model validated with real operational data and considers the dynamic behavior of hotel occupancy and water demand. The results show that the optimized strategy reduces auxiliary heating use by up to 75%, achieving annual energy savings of 8244 kWh, equivalent to 2.3 tons of fuel, and preventing the emission of 10.5 tons of CO₂. This study contributes to the design of sustainable energy systems in the hospitality sector and provides replicable strategies for similar climatic and operational contexts. Full article

(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)

► Show Figures

Figure 1

17 pages, 2320 KiB

Open AccessArticle

Road Obstacle Detection Method Based on Improved YOLOv5

by Pengliu Tan, Zhi Wang and Xin Chang

Algorithms 2025, 18(6), 300; https://doi.org/10.3390/a18060300 - 22 May 2025

Abstract

Road obstacle detection is essential for ensuring the smooth operation of roads and safeguarding the lives and property of travelers. However, current obstacle detection methods face challenges such as missed detections and false positives. To address these issues, an enhanced obstacle detection algorithm based on YOLOv5 (YOLOv5-EC3F) is proposed. First, an effective multi-scale feature fusion module (EMFF) is introduced to extract multi-scale features from the input feature map, providing richer semantic information and enhancing the perceptual range. Second, the SPPF module is replaced with the C3SPPF module to improve the model’s understanding of contextual information and increase its multi-scale adaptability. Experimental results demonstrate that, on the custom dataset, YOLOv5-EC3F raises the mAP by 3 percentage points to 82% and the recall by 7 percentage points to 78%, without compromising precision. This study offers a valuable optimization strategy for the practical application of road obstacle detection. Full article

(This article belongs to the Collection Traditional and Machine Learning Methods to Solve Imaging Problems)

► Show Figures

Figure 1

22 pages, 12260 KiB

Open AccessArticle

Improved Directional Mutation Moth–Flame Optimization Algorithm via Gene Modification for Automatic Reverse Parking Trajectory Optimization

by Yan Chen, Yi Chen, Yang Guo, Longda Wang and Gang Liu

Algorithms 2025, 18(6), 299; https://doi.org/10.3390/a18060299 - 22 May 2025

Abstract

Automatic reverse parking (ARP) faces challenges in finding ideal reference trajectories that avoid collisions, maintain smoothness, and minimize path length. To address this, we propose an improved directional mutation moth–flame optimization algorithm with gene modification (IDMMFO-GM). We develop a practical reference trajectory optimization model by combining cubic spline interpolation with a standardized parking plane coordinate system. To effectively address the infeasible solutions encountered when parking in a garage, we apply gene modification for collision avoidance and berthing tilt generated from the reference trajectory optimization to enhance the preservation of optimization information. Furthermore, we introduce a non-linear decreasing weight coefficient and a directional mutation strategy into the moth–flame optimization algorithm to significantly improve its overall optimization performance. Taking the automatic parking garage space No. 155 in Dalian Shell Museum as the actual vehicle test object, which is situated within Dalian Xinghai Square, test results demonstrate that the proposed algorithm achieves an accelerated optimization speed, enhanced precision in trajectory optimization, and superior tracking control performance. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Algorithms, Volume 18, Issue 6 (June 2025) – 3 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI