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Improved particle swarm optimization by fast annealing algorithm
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Pressure vessel design simulation: Implementing of multi-swarm particle swarm optimization
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Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment
Published 2018“…In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. …”
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Integration of Computer Simulation, Design of Experiments and Particle Swarm Optimization to Optimize the Production Line Efficiency
Published 2016“…The goal of this paper is to optimize the productivity of manufacturing system by integrating computer simulation, design of experiments (DOE) and particle swarm optimization (PSO) algorithm. …”
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Task scheduling in cloud computing using Harris-Hawk Optimization
Published 2024“…In this study, the proposed HHO algorithm is simulated and compared with other well-known swarm intelligence algorithms, including Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). …”
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Proceedings -
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A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems
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Non-probabilistic approach to cooperative position tracking in large swarm of simple mobile robots using triangular cross-observation
Published 2013“…The performance of the algorithm was validated and compared against that of the EKF through series of simulations using Stage multi-agent simulator. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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A Hybrid of Particle Swarm Optimization and Harmony Search to Estimate Kinetic Parameters in Arabidopsis thaliana
Published 2020“…Three performance measurements are used in this paper to evaluate the proposed PSOHS: the standard deviation, nonlinear least squared error, and computational time. The proposed algorithm outperformed the other two methods, namely Simulated Annealing and the downhill simplex method, and proved that PSOHS is a more suitable algorithm for estimating kinetic parameter values.…”
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Simulation of identifying shortest path walkway using particle swarm optimization (PSO)
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