| 1. |
- Ramos-Michel, A., et al.
(författare)
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Improving Metaheuristic Algorithm Design Through Inequality and Diversity Analysis : A Novel Multi-Population Differential Evolution
- 2023
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Ingår i: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). - : IEEE. - 9781665430654 ; , s. 1547-1552
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Konferensbidrag (refereegranskat)abstract
- In evolutionary algorithms and metaheuristics, defining when applying a specific operator is important. Besides, in complex optimization problems, multiple populations can be used to explore the search space simultaneously. However, one of the main problems is extracting information from the populations and using it to evolve the solutions. This article presents the inequality-based multi-population differential evo-lution (IMDE). This algorithm uses the K-means to generate subpopulations (settlements). Two variables are extracted from the settlements, the diversity and the Gini index, which measure the solutions' distribution and the solutions' inequality regarding fitness. The Gini index and the diversity are used in the IMDE to dynamically modify the scalation factor and the crossover rate. Experiments over a set of benchmark functions with different degrees of complexity validate the performance of the IMDE. Besides comparisons, statistical and ranking average validate the search capabilities of the IMDE.
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| 2. |
- Marto, João Pedro, et al.
(författare)
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Safety and Outcome of Revascularization Treatment in Patients With Acute Ischemic Stroke and COVID-19: The Global COVID-19 Stroke Registry.
- 2023
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Ingår i: Neurology. - 1526-632X. ; 100:7
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19-related inflammation, endothelial dysfunction, and coagulopathy may increase the bleeding risk and lower the efficacy of revascularization treatments in patients with acute ischemic stroke (AIS). We aimed to evaluate the safety and outcomes of revascularization treatments in patients with AIS and COVID-19.This was a retrospective multicenter cohort study of consecutive patients with AIS receiving intravenous thrombolysis (IVT) and/or endovascular treatment (EVT) between March 2020 and June 2021 tested for severe acute respiratory syndrome coronavirus 2 infection. With a doubly robust model combining propensity score weighting and multivariate regression, we studied the association of COVID-19 with intracranial bleeding complications and clinical outcomes. Subgroup analyses were performed according to treatment groups (IVT-only and EVT).Of a total of 15,128 included patients from 105 centers, 853 (5.6%) were diagnosed with COVID-19; of those, 5,848 (38.7%) patients received IVT-only and 9,280 (61.3%) EVT (with or without IVT). Patients with COVID-19 had a higher rate of symptomatic intracerebral hemorrhage (SICH) (adjusted OR 1.53; 95% CI 1.16-2.01), symptomatic subarachnoid hemorrhage (SSAH) (OR 1.80; 95% CI 1.20-2.69), SICH and/or SSAH combined (OR 1.56; 95% CI 1.23-1.99), 24-hour mortality (OR 2.47; 95% CI 1.58-3.86), and 3-month mortality (OR 1.88; 95% CI 1.52-2.33). Patients with COVID-19 also had an unfavorable shift in the distribution of the modified Rankin score at 3 months (OR 1.42; 95% CI 1.26-1.60).Patients with AIS and COVID-19 showed higher rates of intracranial bleeding complications and worse clinical outcomes after revascularization treatments than contemporaneous non-COVID-19 patients receiving treatment. Current available data do not allow direct conclusions to be drawn on the effectiveness of revascularization treatments in patients with COVID-19 or to establish different treatment recommendations in this subgroup of patients with ischemic stroke. Our findings can be taken into consideration for treatment decisions, patient monitoring, and establishing prognosis.The study was registered under ClinicalTrials.gov identifier NCT04895462.
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| 3. |
- Morales-Castañeda, Bernardo, et al.
(författare)
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A Novel Diversity-Aware Inertia Weight and Velocity Control for Particle Swarm Optimization
- 2023
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Ingår i: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588
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Konferensbidrag (refereegranskat)abstract
- Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it has shortcomings, such as premature convergence and stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global and local exploration and exploitation capability by determining the influence of the previous velocity on its current motion. Therefore, this article proposes a PSO with a Diversity-aware Inertia and Velocity Control (PSOIVC) algorithm to improve the PSO performance. The PSOIVC employs a novel diversity-aware inertia weight and velocity control approach to tune the parameters to produce a trade-off between exploration and exploitation of the algorithm using the dimension-wise diversity. The PSOIVC algorithm is compared with eight algorithms, including variants of the PSO, on a set of 30 benchmark functions for a single objective real parameter in 30 and 50 dimensions. Based on the results, the proposal presents significant outcomes according to the average values obtained for both comparisons; because it performed similarly or better than the other algorithms in 23/30 and 16/30 for 30 and 50 dimensions, respectively.
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| 4. |
- Morales-Castañeda, Bernardo, et al.
(författare)
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Improving the Convergence of the PSO Algorithm with a Stagnation Variable and Fuzzy Logic
- 2023
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Ingår i: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588
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Konferensbidrag (refereegranskat)abstract
- Particle swarm optimization (PSO) is essential to evolutionary computation algorithms (ECA). The PSO has some drawbacks as premature convergence and stagnation at local minima. Inertia weight is a parameter that controls the global and local exploration and exploitation capability in the PSO by determining the influence of the previous velocity on its current motion. This article proposes using a stagnation counter that verifies the times the PSO is stuck in the same fitness value. In the proposed fuzzy controlled PSO with stagnation coefficient (FCPSO), a fuzzy controller is designed to tune the inertia weight based on the population's diversity and the search's stagnation. This modification allows the PSO to escape from suboptimal values enhancing its search capabilities. The FCPSO is tested over 28 benchmark functions in 50 dimensions. Besides, it has been compared with nine optimization algorithms from the state-of-the-art. The experiments and comparisons suggest that the FCPSO is an interesting tool for solving complex optimization problems.
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| 5. |
- Oliva, Diego, et al.
(författare)
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Segmentation of thermographies from electronic systems by using the global-best brain storm optimization algorithm
- 2023
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Ingår i: Multimedia tools and applications. - : Springer Nature. - 1380-7501 .- 1573-7721. ; 82:29, s. 44911-44941
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Tidskriftsartikel (refereegranskat)abstract
- Segmentation is an important and basic task in image processing. Although no unique method is applicable to all types of images (as thermographies), multilevel thresholding is one of the most widely used techniques for this purpose. Multilevel thresholding segmentation has a major drawback that is to properly find the best configuration of thresholds. For that reason some metaheuristic algorithms are used to optimize the searching for the best thresholds. This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm (GBSO), which improves the standard BSO to find the optimal solutions in complex search spaces. The GBSO uses a population of agents based on a global best and a re-initialization scheme that is triggered by the current state of its population. Here, the GBSO is used to find the best configuration of thresholds by optimizing the minimum cross entropy that is commonly using in image segmentation. Once the best thresholds are obtained they are applied over the images to extract only the regions of interest. For example, in the case of thermographies the parts with higher temperatures. To verify the performance of the proposed method it is firstly applied to classical reference images and after that over thermal images from electronic devices. The idea is to provide an alternative to segment thermographies that permits separating regions with higher temperatures. This could be used as a preprocessing step in a complex image processing system. The experimental result in terms of segmentation of electronic devices in thermographies provides evidence of the good performance of the GBSO. Different comparison with recent methods from the state-of-the-art were conducted where the GBSO obtains 1st place with the best values for the MCET. To validate the quality of segmentation they were used metrics as the peak signal-to-noise ratio (PSNR) where the GBSO is in the 4th rank of comparison, the structural similarity index (SSIM) and the feature similarity index (FSIM). For the FSIM and SSIM the GBSO in the 4th and 3rd rank, respectively.
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