11 papers · ranked by Valyu relevance
Dipti Singh, Neha Chand
This study introduces a novel Parallel Grasshopper Optimization Algorithm (p-GOA), specifically designed to address reliability optimization problems. Although several hybrid algorithms exist in this field, the proposed p-GOA distinctly differs through its parallel cooperative strategy. Unlike sequential methods that…
Tram Nguyen, Snasel Vaclav, Bay Vo, Van Du Nguyen + 1 more
This paper proposes a parallel hybrid metaheuristic, named PH-SHOWOA, that integrates the Spotted Hyena Optimizer (SHO) and the Whale Optimization Algorithm (WOA) to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows (VRPSPDTW). The proposed method leverages the strength of both…
Authors not listed
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
Shujun Peng, Xinhan Lin, Yu Zhang, Yuheng Xiao + 1 more
Parallel scan is a fundamental primitive widely used in a broad range of workloads, including parallel sorting, graph algorithms, and sampling in large language model inference. Although GPU-optimized parallel scan algorithms have been extensively studied, their reliance on vector units makes them inefficient on modern…
Mohammed Alaa Ala’anzy, Nurdaulet Tolendi, Baizhan Baubek, Abdulmohsen Algarni + 1 more
Sorting can be approached in two main ways: sequentially and in parallel. In sequential sorting, data is processed in a single-threaded manner, which can be slow for large datasets. However, parallel sorting divides the task across multiple processing units, enabling faster results by processing data simultaneously.…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Bibo Zhang, Min Wang, Sheng Nie
Point clouds are widely used to represent real-world objects in the fields of augmented/virtual reality, robotics, etc. However, they are often corrupted with noise, hindering downstream tasks such as mesh surface reconstruction, rendering, etc. In this paper, we revisit sparse point cloud representation for denoising.…
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Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
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Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
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Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…