An Improved Particle Swarm Optimization Algorithm

Cheng, Hao; Wang, Jian

doi:10.4028/www.scientific.net/amr.186.454

Cited by 11 publications

(10 citation statements)

References 2 publications

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“…Furthermore, the PSO is simple and easy to implement, which does not have many parameters to adjust and does not need gradient information. PSO is also shown to be better in dealing with nonlinear continuous optimization problems, combinatorial optimization problems, and mixed-integer nonlinear optimization problems [56]. In this study, for the parameter identification of PD model, it is impossible to determine the specific range of initial parameters for identification because the parameters of these virtual patients are different.…”

Section: Discussionmentioning

confidence: 99%

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Liang

Ling-yun

et al. 2019

IEEE Access

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General anesthesia is a critical procedure in clinical surgery. To offer a closed control of depth of hypnosis, we establish a closed-loop anesthetic delivery system for propofol anesthesia. The system consists of three components: 1) three-compartment pharmacokinetic model; 2) a pharmacodynamics model obtained by identifying the relationship of effect-site concentration and Renyi permutation entropy via particle swarm optimization; and 3) ant colony optimization proportion integration differentiation controller. The performance of Renyi permutation entropy and bispectral index in tracking the effect-site concentration is evaluated by the prediction probability. The assessments of the performance of the controller are using: 1) the rising time, percent overshoot, and settling time and 2) median performance error, median absolute performance error, wobble, divergence, and integral absolute error. The results show that the prediction probability of Renyi permutation entropy (0.79±0.13 and mean±standard deviation) is higher than the bispectral index (0.74±0.15). The ant colony optimization proportion integration differentiation controller is quick to respond to sudden changes and maintains the stabilty at the desired depth of hypnosis (rise time and overshoot are 4.53±1.96 min and 3.48±1.49 (%), respectively). In conclusion, the proposed closed-loop anesthetic delivery system has potential value for accurate anesthetic administration.INDEX TERMS General anesthesia, closed-loop anesthetic delivery system, Renyi permutation entropy, ant colony optimization proportional integral derivative, particle swarm optimization.

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Section: Discussionmentioning

confidence: 99%

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Liang

Ling-yun

et al. 2019

IEEE Access

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“…This section outlines the 23 classical test functions that were employed for experiments. The performance of DESMAOA was compared with two newly proposed algorithms (SMA and AOA) and another five very famous optimization algorithms (GWO [38], WOA [39], SSA [40], MVO [5], and PSO [12]). Table 4 lists the main parameter values used in each algorithm.…”

Section: The Classical Benchmark Functionsmentioning

confidence: 99%

“…Swarm-inspired: particle swarm optimization (PSO) [12], emperor penguin optimizer (EPO) [13], Aquila optimizer (AO) [14], remora optimization algorithm (ROA) [4], marine predators algorithm (MPA) [15].…”

mentioning

confidence: 99%

Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization

et al. 2021

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In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.

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“…We have collected eight ensembles near the phase transition region with 2 + 1 flavors of fermions. All the simulations have a 16 3 × 8 space-time volume and a fifth dimension of L S = 32 or 48 and they all lie on a line of constant physics with m π ≈ 200MeV and kaon mass almost physical [7]. Table 1 gives the basic parameters of these finite-temperature ensembles.…”

Section: Implementation Detailsmentioning

confidence: 99%

Dirac Eigenvalue Spectrum at Finite Temperature Using Domain Wall Fermions

Lin¹

2012

Proceedings of XXIX International Symposium on Lattice Field Theory — PoS(Lattice 2011)

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We present a study of the Dirac eigenvalue spectrum near the region of the QCD phase transition. This study makes use of a sequence of ensembles with temperatures from 150 MeV to 200 MeV generated with 2 + 1 flavors of dynamical domain wall fermions (DWF) and the dislocation suppressing determinant ratio (DSDR) action on a 16 3 × 8 lattice with an extent of 32 or 48 in the fifth dimension. All the simulations lie on a line of constant physics with 200 MeV pions. The DWF Dirac operator is normalized using the methods of Giusti and Luscher combined with those of Rome-Southampton collaboration, allowing a direct evaluation of the Banks-Casher relation. The relation between the resulting temperature-dependent Dirac eigenvalue spectrum and the possible restoration of U(1) A symmetry with increasing temperature is discussed.

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An Improved Particle Swarm Optimization Algorithm

Cited by 11 publications

References 2 publications

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization

Dirac Eigenvalue Spectrum at Finite Temperature Using Domain Wall Fermions

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