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10 articles for “Parallel genetic algorithm”
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Parallel Genetic Algorithm for Fuel Bundle Burnup Optimization of Pressurized Heavy Water Reactor
Abstract: In this study, parallel genetic algorithm (GA) was applied to solve an optimization problem of nuclear fuel management pertaining to a 220 MWe pressurized heavy water reactor (PHWR). The study aims at finding appropriate values of reference discharge burnups of two zones of the reactor core which gives maximum fuel economy, while satisfying operational and safety-related constraints. This is a multiobjective optimization problem with four objectives and four constraints. Penalty …
Published in Journal of Nuclear Engineering & Technology · Vol. 6, Issue 1, 2016 · pp. 11–24 Read article
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A Survey of Genetic Algorithm Applications in Nuclear Fuel Management
Abstract: The prime aim of nuclear fuel management is to achieve higher fuel utilization without compromising safety during reactor operation. Nuclear fuel management optimization is a classical nuclear engineering problem, which has been studied for more than four decades and several techniques have been used for its solution. Genetic algorithm is one among the major global optimization techniques, used in the field of nuclear fuel management. Aim of this paper is …
Published in Journal of Nuclear Engineering & Technology · Vol. 4, Issue 1, 2014 · pp. 45–62 Read article
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Parallelizing Mutations for Genetic Algorithm
Abstract: In the present work, the author has implemented the mutations crossovers of genetic algorithm (GA) over a parallelism environment. This attempt will enable us collect data to build a framework for shared parallelism for the solution of equations in the same verifiable computational time. The data indicate towards the better implementation of a larger computational problem in the parallelized for the better solution in the verifiable execution time.Keywords: Genetic algorithm, …
Published in Recent Trends in Parallel Computing · Vol. 1, Issue 3, 2014 · pp. 7–9 Read article
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Optimization of Production Scheduling using Multi-Tasking by Multi-Population Genetic Algorithm
Abstract: Production scheduling is an important activity for manufacturing and engineering, where it can have a major impact on the productivity of a production system. Traditional in-house manufacturing involves production machines with dedicated operators. Here, multitasking technique is used to optimize the production scheduling problem. In parallel machine scheduling, there are n jobs and m machines and each job needs to be executed on one of the machines during a fixed …
Published in Journal of Production Research & Management · Vol. 5, Issue 1, 2015 · pp. 18–26 Read article
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Automating Compiler Optimization: A Machine Learning Approach
Abstract: This study reports on an ML-based approach to compiler optimization, complementing traditional optimization methods that rely strongly on hand-tuned settings. Compiler optimization plays a key role in performance-speedup and energy optimization of complex contemporary software systems. However, the traditional approach to optimizer settings involves laborious, error-prone, and scale-insensitive human-in-the-loop intervention, especially in the complex and high-demand environments in which today's computing application thrives. By integrating RL and GA, we can …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 12–16 Read article
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Accelerating Unsupervised Feature Learning: Parallelized Training of Denoising Autoencoders
Abstract: Unsupervised representation learning has become a cornerstone of contemporary machine learning, enabling algorithms to extract informative features from un-labelled, high-dimensional data. This work investigates the efficacy of stacked denoising autoencoders (SDAEs) trained via parallelized stochastic gradient descent (SGD) as a scalable approach to feature extraction. By strategically leveraging multi-threaded computation, our study systematically examines the trade-offs between increased parallelism, training efficiency, and the preservation of model accuracy. Experiments on the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 56–64 Read article
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A Survey of Edge Detection and Object Segmentation from Contour based Soft Computing Approaches
Abstract: Soft computing approaches such as consisting of fuzzy logic, neural or evolutionary computation are the emerging field with the wide applications in edge detection and image segmentation. Such a process of partitioning the pixels regions of the digital images with boundary between two homogenous regions refers to the detection of edges. There was various approaches that have been implemented to achieve the same but their performance and its scope of …
Published in Recent Trends in Parallel Computing · Vol. 2, Issue 1, 2015 · pp. 16–20 Read article
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Optimal PMU Placement in Power Systems Using Graph Theory and PSAT
Abstract: Phasor measurement units (PMUs) play a vital role in modern power systems by delivering synchronized, real-time measurements. These devices enhance system reliability by supporting functions such as monitoring, protection, and control. By accurately capturing voltage and current phasors across different locations, PMUs enable better situational awareness and more effective decision-making in grid operations and management. Determining the optimal location of PMUs is essential to ensure system observability, reduce installation costs, …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 26–32 Read article
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Enhancing Production Line Efficiency: Simulating and Optimizing Single and Parallel Line Processes
Abstract: During a time of fast-paced industrial growth, increasing production line effectiveness is a core issue for manufacturers looking to maximize output, reduce waste, and stay competitive. This study explores the use of simulation-based optimization methods to enhance single and parallel production line designs. Stepping beyond traditional trial-and-error methods, the research utilizes Siemens Tecnomatix Plant Simulation to simulate actual manufacturing scenarios, considering intricacies like buffer capacities, machine sequencing, and event-driven scheduling. …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 22–32 Read article
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The Convergence of AI and Composites - A Review Anchored in Patent Trends
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article