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Evaluation of advanced AI models for accurate prediction and uncertainty analysis of aeration inception in hydrofoil-crested stepped spillways. [PDF]
Sci RepNejatipour P +6 more
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WS-SSA: workflow scheduling in cloud computing using salp swarm algorithm. [PDF]
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Neurosurgery Clinics of North America, 2023
The MIS PSO is a hybrid approach using less-invasive surgical approaches combined with traditional open spinal deformity techniques for the treatment of adult spinal deformities. The MIS PSO allows for the correction of spinal deformities in the coronal and sagittal planes, even in the fixed spine, and simultaneously mitigating the morbidity of ...
Oliver G S, Ayling, Michael Y, Wang
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The MIS PSO is a hybrid approach using less-invasive surgical approaches combined with traditional open spinal deformity techniques for the treatment of adult spinal deformities. The MIS PSO allows for the correction of spinal deformities in the coronal and sagittal planes, even in the fixed spine, and simultaneously mitigating the morbidity of ...
Oliver G S, Ayling, Michael Y, Wang
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PSO-sono: A novel PSO variant for single-objective numerical optimization
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhenyu Meng +3 more
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Effect of the PSO Topologies on the Performance of the PSO-ELM
2012 Brazilian Symposium on Neural Networks, 2012In recent years, the Extreme Learning Machine (ELM) has been hybridized with the Particle Swarm Optimization (PSO). This hybridization is named PSO-ELM. In most of these hybridizations, the PSO uses the global topology. However, other topologies were designed to improve the performance of the PSO.
Elliackin M. N. Figueiredo +1 more
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Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006
Spatial Extension PSO (SEPSO) and Attractive-Repulsive PSO (ARPSO) are methods for artificial injection of diversity into particle swarm optimizers that are intended to encourage converged swarms to engage in exploration. While simple to implement, effective when tuned correctly, and benefiting from intuitive appeal, SEPSO behavior can be improved by ...
Christopher K. Monson, Kevin D. Seppi
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Spatial Extension PSO (SEPSO) and Attractive-Repulsive PSO (ARPSO) are methods for artificial injection of diversity into particle swarm optimizers that are intended to encourage converged swarms to engage in exploration. While simple to implement, effective when tuned correctly, and benefiting from intuitive appeal, SEPSO behavior can be improved by ...
Christopher K. Monson, Kevin D. Seppi
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Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2004
In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are ...
R. Brits +2 more
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In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are ...
R. Brits +2 more
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An intelligent PSO watermarking
2010 International Conference on Machine Learning and Cybernetics, 2010Meerwald et. al. [2] revealed that the method proposed in [1] exists potential insecurity. This paper proposes an intelligent watermarking by invoking particle swarm optimization (PSO) technique in wavelet domain to overcome the revealed insecurity issue, furthermore resolve the conflict between imperceptibility and robustness of watermarking ...
Yuh-Rau Wang +2 more
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Cellular PSO: A PSO for Dynamic Environments
2009Many optimization problems in real world are dynamic in the sense that the global optimum value and the shape of fitness function may change with time. The task for the optimization algorithm in these environments is to find global optima quickly after the change in environment is detected. In this paper, we propose a new hybrid model of particle swarm
Ali B. Hashemi, Mohammad Reza Meybodi
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