Search Results - parallel optimization ((((method algorithm) OR (swarm algorithm))) OR (based algorithm))
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Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system
Published 2010“…The particle swarm optimization (PSO) method is used to tune the parameters of the controller and weighting functions subject to QFT and/or constraints. …”
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Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
Published 2022“…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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Single and Multiple variables control using Tree Physiology Optimization
Published 2017“…The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). …”
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…After that, SKF is tested to find the most accurate image template matching and compared with Particle Swarm Optimization (PSO) and Bat Algorithm with Mutation (BAM). …”
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A hybrid bat–swarm algorithm for optimizing dam and reservoir operation
Published 2019“…This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). …”
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Single-objective and multi-objective optimization algorithms based on sperm fertilization procedure / Hisham Ahmad Theeb Shehadeh
Published 2018“…These algorithms are Genetic Algorithms (GA), Parallel Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO). …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. …”
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A parallel ensemble learning model for fault detection and diagnosis of industrial machinery
Published 2023“…The base learners adopt a hybrid Back-Propagation (BP) and Particle Swarm Optimization (PSO) algorithms to exploit the corresponding local and global optimization capabilities for identifying optimal features and improving FDD performance. …”
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Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
Published 2014“…Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. …”
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Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
Published 2013“…Performance of the QPSO technique based on many heuristic algorithms it is comprised the following steps. …”
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PMT: opposition-based learning technique for enhancing meta-heuristic performance
Published 2019“…To evaluate the PMT's performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. …”
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Voting algorithms for large scale fault-tolerant systems
Published 2011“…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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Predictive modeling of condominium prices using a Particle Swarm Optimization-Random Forest approach / Che Wan Sufia Che Wan Samsudin
Published 2025“…Essential phases of the project include data collection, data preprocessing, and the implementation of the Particle Swarm Optimization-Random Forest price prediction algorithm. …”
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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…For these reasons, to improve the time and accuracy of the coverage in population-based meta-heuristics and their utilization in HPAs, this thesis presents a novel optimization algorithm called the Raccoon Optimization Algorithm (ROA). …”
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Review of the grey wolf optimization algorithm: variants and applications
Published 2023“…One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. …”
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A GPGPU Approach to Accelerate Ant Swarm Optimization Rough Reducts (ASORR) Algorithm
Published 2012“…In order to find a reducts, a such applications of rough set uses a discernibility matrix. Ant Swarm Optimization Rough Reducts (ASORR) algorithm is used in rough reducts calculation for identifying significant attribute set optimally. …”
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Review of the grey wolf optimization algorithm: variants and applications
Published 2024“…One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. …”
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Hybrid flow shop scheduling with energy consumption in machine shop using moth flame optimization
Published 2022“…Optimization using MFO has been conducted and the results was compared with well-established algorithm like Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization. …”
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Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems
Published 2018“…Additionally, the MTS algorithm is also implemented in parallel computing to produce parallel MTS for generating comparable solutions in shorter computational times. …”
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