The selection of parameters for the hybrid active power filter (HAPF) is essential to harmonic compensation. To optimize HAPF parameters, this paper presents an improved grasshopper optimization algorithm (IGOA). In the IGOA, the whole population is divided into two sub-populations which focus on exploration and exploitation respectively. An improved social interaction mechanism is proposed to balance global and local searches. Furthermore, a learning strategy is introduced and an exemplar pool is built to replace the target in the original GOA, which can enhance the global search ability and escape local optima. The proposed IGOA is employed to optimize the parameters of two prevalent HAPF topologies for several cases. The experimental results show that the IGOA can get a promising performance compared with previous studies and other meta-heuristic algorithms.
Hybrid power active filter (HAPF) is an important device to suppress the harmonics of the power system. In HAPF, the parameters estimation has a great impact on ensuring the power quality in the power system. Aiming at the problem of minimizing the harmonic pollution (HP) in the power system, this paper proposes a new technology namely IEDA for parameter optimization of hybrid power active filters, which is an improved dragonfly algorithm (DA) with higher exploitation capability. DA is a global search algorithm with sufficient ability to avoid falling into local optimization, however, DA performs poorly for local search. In the IEDA, we adopt a strategy of division of labor to divide particles into exploitation population and exploration population. In the exploitation population, we introduce the information exchange mechanism of the differential evolution (DE) and set up an exemplar pool to enhance its exploitation capability. In the exploration population, we use the global search ability of the DA to prevent particles from falling into a local optimum. Through the division of labor between exploitation population and exploration population, the problems of low accuracy and slow convergence of DA are effectively solved. Experimental results show that the algorithm has greatly improved accuracy and reliability compared with seven well-established algorithms.
Background Airway management, including noninvasive endotracheal intubation or invasive tracheostomy, is an essential treatment strategy for patients with deep neck space abscess (DNSA) to reverse acute hypoxia, which aids in avoiding acute cerebral hypoxia and cardiac arrest. This study aimed to develop and validate a novel risk score to predict the need for airway management in patients with DNSA. Methods Patients with DNSA admitted to 9 hospitals in Guangdong Province between January 1, 2015, and December 31, 2020, were included. The cohort was divided into the training and validation cohorts. The risk score was developed using the least absolute shrinkage and selection operator (LASSO) and logistic regression models in the training cohort. The external validity and diagnostic ability were assessed in the validation cohort. Results A total of 440 DNSA patients were included, of which 363 (60 required airway management) entered into the training cohort and 77 (13 required airway management) entered into the validation cohort. The risk score included 7 independent predictors (p < 0.05): multispace involvement (odd ratio [OR] 6.42, 95% confidence interval [CI] 1.79–23.07, p < 0.001), gas formation (OR 4.95, 95% CI 2.04–12.00, p < 0.001), dyspnea (OR 10.35, 95% CI 3.47–30.89, p < 0.001), primary region of infection, neutrophil percentage (OR 1.10, 95% CI 1.02–1.18, p = 0.015), platelet count to lymphocyte count ratio (OR 1.01, 95% CI 1.00–1.01, p = 0.010), and albumin level (OR 0.86, 95% CI 0.80–0.92, p < 0.001). Internal validation showed good discrimination, with an area under the curve (AUC) of 0.951 (95% CI 0.924–0.971), and good calibration (Hosmer–Lemeshow [HL] test, p = 0.821). Application of the clinical risk score in the validation cohort also revealed good discrimination (AUC 0.947, 95% CI 0.871–0.985) and calibration (HL test, p = 0.618). Decision curve analyses in both cohorts demonstrated that patients could benefit from this risk score. The score has been transformed into an online calculator that is freely available to the public. Conclusions The risk score may help predict a patient’s risk of requiring airway management, thus advancing patient safety and supporting appropriate treatment.
In view of various issues occasioned by harmonic pollution in the power system, the optimization of the filter parameters is of great significance. However, under the system constraints, estimating the filter parameters accurately and reliably is a challenging task. To complete this task, this paper proposes an improved salp swarm algorithm based on the spiral flight search strategy (ISSA-SFS), which rationally integrates the spiral flight search (SFS) strategy, the multiple leader (ML) strategy, and the random learning (RL) strategy with two improve evolution phases: the improved lead phase (ILP) and the improved follow phase (IFP). In the ILP, the SFS strategy is introduced to enhance the global search capability and avoid premature convergence. Furthermore, in the ILP, an ML strategy is proposed to select multiple leaders, further strengthening the global search capability of the proposed algorithm. In the IFP, a simple RL strategy is developed to learn two different random individuals, efficiently improving the local exploitation. The proposed algorithm ISSA-SFS is applied to optimize two prominent hybrid active power filter (HAPF) topologies, and each topology contains four different study cases. The overall experimental results indicate that the ISSA-SFS is a more promising alternative to achieve the optimal design of HAPFs compared with other well-established algorithms, especially in terms of accuracy and robustness. INDEX TERMS Parameter optimization, Harmonic pollution (HP), Hybrid active power filter (HAPF), Salp swarm algorithm (SSA), Spiral flight search (SFS).
The effective selection of Variable Cycle Engine (VCE) parameters plays a key role in achieving low specific fuel consumption (SFC) of fighters. However, the selection of VCE parameters is a continuous multimodal issue involving substantial local optima, so that most swarm intelligence (SI) algorithms are easily trapped into local optimal solutions, and cannot obtain satisfactory performance. To address this problem, an improved moth flame optimization algorithm with adaptive Lévy-Flight perturbations (ALFMFO) is proposed. In ALFMFO, the current population aggregation status can be accurately judged based on the difference in fitness variance between two successive moth generations. According to the population aggregation status, the Lévy-Flight disturbance strategy can adaptively adjust the perturbation probability to enhance the ability of ALFMFO to escape from local optimal solutions and realize the minimum SFC optimization of VCE. Experimental results suggest that ALFMFO is effective and superior to other compared SI algorithms in terms of accuracy and robustness.
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