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Multi-level parallel chaotic Jaya optimization algorithms for solving constrained engineering design problems

JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 12280-12319页

作者： Migallon, H. Jimeno-Morenilla, A. Rico, H. Sanchez-Romero, J. L. Belazi, A. Miguel Hernandez Univ Dept Comp Engn Elche 03202 Spain Univ Alicante Dept Comp Technol Alicante 03071 Spain Tunis El Manar Univ Lab RISC ENIT LR 16 E507 Tunis 1002 Tunisia

Several heuristic optimization algorithms have been applied to solve engineering problems. Most of these algorithms are based on populations that evolve according to different rules and parameters to reach the optimal value of a function cost through an iterative process. Different parallel strategies have been proposed to accelerate these algorithms. In this work, we combined coarse-grained strategies, based on multi-populations, with fine-grained strategies, based on a diffusion grid, to efficiently use a large number of processes, thus drastically decreasing the computing time. The Chaotic Jaya optimization algorithm has been considered in this work due to its good optimization and computational behaviors in solving both the constrained optimization engineering problems (seven problems) and the unconstrained benchmark functions (a set of 18 functions). The experimental results show that the proposed parallel algorithms outperform the state-of-the-art algorithms in terms of optimization behavior, according to the quality of the obtained solutions, and efficiently exploit shared memory parallel platforms.

关键词： Optimization Constrained engineering problem Jaya algorithm Chaotic map parallel algorithms OpenMP

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parallel Inspection Route Optimization With Priorities for 5G Base Station Networks

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10860-10870页

作者： Dai, Xiangqi Liang, Zhenglin Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

5G base station networks generate numerous alarms daily. With the increasing demand for digital services, it is vital to inspect and rectify anomalies to uphold user satisfaction. This study explores the potential of unmanned aerial vehicle (UAV) empowered opportunistic inspection based on alarm data. We formulate the inspection routing problem as a prioritized traveling salesman problem (PTSP) encompassing two categories of base stations. Priority is assigned to stations generating more alarms, while others are subject to opportunistic inspection. To expedite large-scale opportunistic inspection routes, we introduce a novel transformer-based parallelizable routing algorithm (TPRA). TPRA is an intelligent optimization that orchestrates multiple parallelized constrained reinforcement learning algorithms. Through balancing spectral clustering, the large-scale graph is segmented into manageable subgraphs. For each subgraph, the prioritized inspection routing problem is formulated as a constrained Markov decision process and optimized by transformer-based reinforcement learning in parallel. The optimized subgraphs are then merged using an adaptive large neighborhood search approach. Through parallel computing, our approach achieves as much as 75% reduction in computation time, while concurrently generating shorter routes. The approach is implemented in real-world cases to validate its efficacy. Note to Practitioners-The rapid expansion of 5G infrastructure underscores the critical need for advanced technology and maintenance strategies. Base stations are often placed at high altitudes to ensure line-of-sight connectivity, which poses difficulties for maintenance, particularly in challenging terrains. UAVs offer a promising solution for faster and safer inspection and rectification. The designed approach utilizes reinforcement learning in parallel to optimize UAV inspection routes in an opportunistic manner. This method strategically prioritizes inspection routes based o

关键词： Communication system operations and management optimization methods traveling salesman problems parallel algorithms unmanned aerial vehicle Communication system operations and management optimization methods traveling salesman problems parallel algorithms unmanned aerial vehicle

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Fast parallel CPU-GPU Approximate Spectral Clustering for Transcriptomics Data

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2025年第1期53卷 1-25页

作者： Brankovic, Stefan Smiljkovic, Lazar Obradovic, Predrag Radonjiic, Milos Misic, Marko Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia MGI Tech Belgrade 11000 Serbia

Spectral clustering algorithms have been used in various research domains to discover structure and patterns in data. However, high computational and space complexity hinders their usage for large-scale datasets in machine learning and bioinformatics. Various approximate spectral clustering methods were proposed in the open literature to solve those problems. In this paper, we describe our GPU-based, parallel implementation of an approximate spectral algorithm based on the Nystrom method and column sampling and its memory-efficient variant. We evaluate our solution using several annotated datasets, such as USPS, MNIST, and MNIST8, as well as bioinformatics data, especially from the domain of single-cell and spatial transcriptomics. We obtain speedups of up to 31.8x depending on the dataset used and demonstrate the scalability of the solution for the datasets with up to four million samples.

关键词： Approximate spectral clustering GPU computing parallel algorithms parallel programming Performance analysis

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A Unified parallel Framework for LUT Mapping and Logic Optimization

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 214-226页

作者： Liu, Tianji Sun, Yang Chen, Lei Li, Xing Yuan, Mingxuan Young, Evangeline F. Y. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Huawei Huawei Noahs Ark Lab Hong Kong Peoples R China

Lookup-table (LUT) mapping has been extensively utilized in logic synthesis, including being an indispensable step in FPGA design, serving as a building block in high-effort synthesis flows, and providing an algorithmic framework for logic optimization. Hence, a fast mapping algorithm is vital to satisfying the demand for synthesizing high-quality, large-scale modern VLSI designs. This article proposes two efficient GPU-parallel algorithms, namely LUT mapping and and-inverter graph (AIG) optimization using a precomputed database, which rely on a common parallel mapping framework that consists of novel fine-grained parallel mapping passes with high degree of parallelism. The mapping pass is enhanced by specifically tailored cut evaluation and memory management methods for GPUs that enable fast mapping of large circuits with limited GPU memory. parallel timing analysis passes and parallel cut expansion passes are also proposed for constructing a fully GPU-accelerated LUT mapping flow. The core of parallel AIG optimization is a plugin of the mapping framework, which contains a self-adaptive parallel candidate structure evaluation procedure with high time efficiency and low hardware resource usage. Experiments show that on average, GPU LUT mapping and AIG optimization achieve 34.6x and 99.9x speedup with similar result quality, compared with the high-performance LUT mapper and AIG optimization algorithm with a database implemented in ABC, respectively, on large benchmarks. When combining the two algorithms with other GPU logic optimization algorithms, a GPU-based sequence targeting LUT network synthesis achieves 46.7x speedup with 4.7% smaller area and 0.2% smaller delay over ABC.

关键词： Table lookup Logic Optimization Delays Graphics processing units Databases Memory management GPU logic optimization logic synthesis parallel algorithms technology mapping

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Automated Guided Vehicle Scheduling Problem in Manufacturing Workshops: An Adaptive parallel Evolutionary Algorithm

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7361-7372页

作者： Li, Zhongkai Pan, Quanke Miao, Zhonghua Sang, Hongyan Li, Weimin Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

In the realm of scheduling problems, metaheuristics have been widely embraced as superior solutions, appreciated for their ability to generate resolutions for non-deterministic polynomial-time hard (NP-hard) problems swiftly. This paper presents a novel parallel evolutionary algorithm (PEA), which marries metaheuristics and parallel computing to amplify computer performance utilization. Four operators and a restart strategy are incorporated into the proposed PEA to bolster both its global and local search capabilities. An accelerated calculation method for two operators is proposed. The algorithm also features an adaptive method that generates sub-threads and parameters based on computer performance, along with rotation for evaluating solutions. A random search sub-thread is established to update the solution. The algorithm is tested on the workshop automated guided vehicle (AGV) scheduling problem and compared against other optimization algorithms to ascertain its efficacy. The test results overwhelmingly highlight the superior performance of the proposed algorithm. Note to Practitioners-The paper introduces a novel parallel evolutionary algorithm (PEA) for scheduling problems, which combines metaheuristics and parallel computing to enhance computer performance utilization. The algorithm incorporates four operators and a restart strategy, along with an accelerated calculation method for two operators. It also includes an adaptive method to generate sub-threads and parameters based on computer performance, as well as rotation for evaluating solutions. A random search sub-thread is established to update the solution. The proposed algorithm is tested on the workshop automated guided vehicle (AGV) scheduling problem, producing superior results compared to other optimization algorithms. Its ability to swiftly generate resolutions for NP-hard problems can greatly benefit industries that rely on efficient scheduling, such as logistics and manufacturing. However, it is imp

关键词： Task analysis Job shop scheduling Processor scheduling Metaheuristics Scheduling parallel algorithms Costs Evolutionary algorithm parallel processing computational efficiency automated guided vehicle

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parallel Multi Objective Shortest Path Update Algorithm in Large Dynamic Networks

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2025年第5期36卷 932-944页

作者： Shovan, S. M. Khanda, Arindam Das, Sajal K. Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building on this, we propose DynaMOSP, a parallel heuristic for Dynamic Multi Objective Shortest Path searches in large, fully dynamic networks. We provide a theoretical analysis of the conditions to achieve Pareto optimality. Furthermore, we devise a dedicated shared memory CPU implementation along with a version for heterogeneous computing environments. Empirical analysis on eight real-world graphs demonstrates that our method scales effectively. The shared memory CPU implementation achieves an average speedup of 12.74x and a maximum of 57.22x, while on an Nvidia GPU, it attains an average speedup of 69.19x, reaching up to 105.39x when compared to state-of-the-art techniques.

关键词： Heuristic algorithms Pareto optimization parallel algorithms Graphics processing units Wireless sensor networks Optimization Linear programming Aerodynamics Search problems Scalability Multi-objective shortest path dynamic graph GPU shared-memory SYCL programming model

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Improved parallel Algorithm for Finding Minimum Cuts in Stochastic Flow Networks

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IEEE LATIN AMERICA TRANSACTIONS 2025年第4期23卷 285-293页

作者： Joshan, Mohammad Saito, Jose Pedrino, Emerson Univ Fed Sao Carlos Sao Carlos SP Brazil

This article solves the convergence problem in the parallel BK algorithm for large-scale flow networks. We introduce a merging method and a pseudo-Boolean representation-based invariance analysis that optimize the algorithm's performance compared to classical approaches such as Ford-Fulkerson, Edmonds-Karp, Push-Relabel, and Kargers Algorithm. Our approach improves energy efficiency, reduces memory usage, and lowers time complexity by leveraging parallelization techniques. We evaluate the performance of these algorithms using Python simulations on various benchmark graphs, ranging from small to large-scale networks. The results show that our method reduces memory consumption by up to 40% and speeds up execution time by 30%, while maintaining high accuracy in finding minimum cuts. This paper demonstrates the algorithm's potential for applications in image segmentation, wireless sensor networks, and network reliability analysis

关键词： Heuristic algorithms Merging parallel algorithms Convergence Accuracy Partitioning algorithms Optimization Graph theory Computational efficiency Reliability Stochastic processes parallel Algorithm Connectivity Min-Cuts Stochastic-Flow Networks Network Reliability

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algorithms for parallel Generic hp-Adaptive Finite Element Software

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 2023年第3期49卷 25-25页

作者： Fehling, Marc Bangerth, Wolfgang Colorado State Univ Dept Math 1874 Campus Delivery Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO USA Colorado State Univ Dept Geosci Ft Collins CO USA

The hp-adaptive finite element method-where one independently chooses the mesh size (h) and polynomial degree (p) to be used on each cell-has long been known to have better theoretical convergence properties than either h- or p-adaptive methods alone. However, it is not widely used, owing at least in part to the difficulty of the underlying algorithms and the lack of widely usable implementations. This is particularly true when used with continuous finite elements. Herein, we discuss algorithms that are necessary for a comprehensive and generic implementation of hp-adaptive finite element methods on distributed-memory, parallel machines. In particular, we will present a multistage algorithm for the unique enumeration of degrees of freedom suitable for continuous finite element spaces, describe considerations for weighted load balancing, and discuss the transfer of variable size data between processes. We illustrate the performance of our algorithms with numerical examples and demonstrate that they scale reasonably up to at least 16,384 message passage interface processes. We provide a reference implementation of our algorithms as part of the open source library ***.

关键词： parallel algorithms hp-adaptivity finite element methods high performance computing

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A novel parallel framework for scatter search

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KNOWLEDGE-BASED SYSTEMS 2025年 314卷

作者： Casado, A. Perez-Pelo, S. Sanchez-Oro, J. Duarte, A. Laguna, M. Univ Rey Juan Carlos C Tulipan S-N Mostoles 28933 Spain Univ Colorado Boulder Leeds Sch Business Boulder CO 80309 USA

Scatter search (SS) is a well-established metaheuristic for hard combinatorial optimization problems. SS is characterized by its versatility and ease of context adaptation and implementation. Although the literature includes SS parallelization schemes for specific problems, a general parallel framework for scatter search has not been developed and tested. We introduce three SS parallel designs, each focusing on a different task, namely, reducing computational time, increasing search exploration, and balancing search intensification and diversification. The proposed designs are tested on problems where the state of the art is a traditional (sequential) SS approach. This testing platform helps us assess the contributions of the parallel computing strategies to solution speed and quality. Our publicly available code is designed to be adapted to optimization problems that are not considered here. The results show promising avenues for establishing a general framework of SS parallelization.

关键词： Scatter search parallel algorithms Capacitated dispersion problem MaxCut problem Profile minimization problem

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A parallel algorithm for counting parse trees ☆

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INFORMATION AND COMPUTATION 2025年 303卷

作者： Mikhelson, Margarita Okhotin, Alexander St Petersburg State Univ Dept Math & Comp Sci 7 9 Univ Skaya Nab St Petersburg 199034 Russia

A parallel algorithm for computing the number of parse trees of a given string according to a fixed context-free grammar is defined. More generally, the algorithm applies to computing the weight of a string in a weighted grammar over any semiring. The algorithm is first implemented on an arithmetic circuit of depth at most 6(log2n)2 + O ( log n ) and with O ( n 6 ) elements, where the constant factors in the big-O notation depend on the grammar. Then, the circuit is improved using fast matrix multiplication to use only O ( n 5.38 ) elements, while preserving depth O (( log n ) 2 ). (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Context-free grammars Weighted grammars Parsing parallel algorithms Matrix multiplication

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