Evolutionary feature selection (FS) methods face the challenge of "curse of dimensionality" when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size ...cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of "divide and conquer" in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.
For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, and the total distance of routes were normally considered as the optimization ...objectives. Except for the above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, a corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, a robust dynamic multi-objective vehicle routing method with two-phase is proposed . Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii) The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii) A metric measuring the algorithms robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.
Previous methods of designing a bolt supporting network, which depend on engineering experiences, seek optimal bolt supporting schemes in terms of supporting quality. The supporting cost and time, ...however, have not been considered, which restricts their applications in real-world situations. We formulate the problem of designing a bolt supporting network as a three-objective optimization model by simultaneously considering such indicators as quality, economy, and efficiency. Especially, two surrogate models are constructed by support vector regression for roof-to-floor convergence and the two-sided displacement, respectively, so as to rapidly evaluate supporting quality during optimization. To solve the formulated model, a novel interactive preference-based multiobjective evolutionary algorithm is proposed. The highlight of generic methods which interactively articulate preferences is to systematically manage the regions of interest by three steps, that is, "partitioning-updating-tracking" in accordance with the cognition process of human. The preference regions of a decision-maker (DM) are first articulated and employed to narrow down the feasible objective space before the evolution in terms of nadir point, not the commonly used ideal point. Then, the DM's preferences are tracked by dynamically updating these preference regions based on satisfactory candidates during the evolution. Finally, individuals in the population are evaluated based on the preference regions. We apply the proposed model and algorithm to design the bolt supporting network of a practical roadway. The experimental results show that the proposed method can generate an optimal bolt supporting scheme with a good balance between supporting quality and the other demands, besides speeding up its convergence.
Purpose
Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) ...and deep learning based on CT images to predict MVI preoperatively.
Methods
In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models.
Results
Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923–0.973) and 0.980 (95% CI 0.959–0.993), respectively (
p
= 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797–0.947) and 0.906 (95% CI 0.821–0.960), respectively (
p
= 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months,
p
< 0.001; 3D-CNN Model: 64.06 vs. 31.05 months,
p
= 0.027).
Conclusion
The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.
Background
In pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required.
Purpose
To develop a radiomic nomogram based on MR ...radiomics to stratify patients preoperatively and potentially improve clinical practice.
Study Type
Retrospective.
Population
We enrolled 303 patients from two medical centers. Patients with a disease‐free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126).
Field Strength/Sequence
3.0T axial T1‐weighted (T1‐w), T2‐weighted (T2‐w), contrast‐enhanced T1‐weighted (CET1‐w).
Assessment
ER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19‐9, and radiomic‐related features of ER were assessed. In addition, to determine the intra‐ and interobserver reproducibility of radiomic features extraction, the intra‐ and interclass correlation coefficients (ICC) were calculated.
Statistical Tests
The area under the receiver‐operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit.
Results
The AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19‐9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19‐9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort).
Data Conclusion
The radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer.
Level of Evidence: 4
Technical Efficacy: Stage 4
J. Magn. Reson. Imaging 2020;52:231–245.
Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input ...weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.
Feature selection is an important data preprocessing technique in multi-label classification. Although a large number of studies have been proposed to tackle feature selection problem, there are a ...few cases for multi-label data. This paper studies a multi-label feature selection algorithm using an improved multi-objective particle swarm optimization (PSO), with the purpose of searching for a Pareto set of non-dominated solutions (feature subsets). Two new operators are employed to improve the performance of the proposed PSO-based algorithm. One operator is adaptive uniform mutation with action range varying over time, which is used to extend the exploration capability of the swarm; another is a local learning strategy, which is designed to exploit the areas with sparse solutions in the search space. Moreover, the idea of the archive, and the crowding distance are applied to PSO for finding the Pareto set. Finally, experiments verify that the proposed algorithm is a useful approach of feature selection for multi-label classification problem.
Software project scheduling is the problem of allocating employees to tasks in a software project. Due to the large scale of current software projects, many studies have investigated the use of ...optimization algorithms to find good software project schedules. However, despite the importance of human factors to the success of software projects, existing work has considered only a limited number of human properties when formulating software project scheduling as an optimization problem. Moreover, the changing environments of software companies mean that software project scheduling is a dynamic optimization problem. However, there is a lack of effective dynamic scheduling approaches to solve this problem. This work proposes a more realistic mathematical model for the dynamic software project scheduling problem. This model considers that skill proficiency can improve over time and, different from previous work, it considers that such improvement is affected by the employees’ properties of motivation and learning ability, and by the skill difficulty. It also defines the objective of employees’ satisfaction with the allocation. It is considered together with the objectives of project duration, cost, robustness and stability under a variety of practical constraints. To adapt schedules to the dynamically changing software project environments, a multi-objective two-archive memetic algorithm based on Q-learning (MOTAMAQ) is proposed to solve the problem in a proactive-rescheduling way. Different from previous work, MOTAMAQ learns the most appropriate global and local search methods to be used for different software project environment states by using Q-learning. Extensive experiments on 18 dynamic benchmark instances and 3 instances derived from real-world software projects were performed. A comparison with seven other meta-heuristic algorithms shows that the strategies used by our novel approach are very effective in improving its convergence performance in dynamic environments, while maintaining a good distribution and spread of solutions. The Q-learning-based learning mechanism can choose appropriate search operators for the different scheduling environments. We also show how different trade-offs among the five objectives can provide software managers with a deeper insight into various compromises among many objectives, and enabling them to make informed decisions.
Patients with prior illness are more vulnerable to heat stroke-induced injury, but the underlying mechanism is unknown. Recent studies suggested that NLRP3 inflammasome played an important role in ...the pathophysiology of heat stroke.
In this study, we used a classic animal heat stroke model. Prior infection was mimicked by using lipopolysaccharide (LPS) or lipoteichoic acid (LTA) injection before heat stroke (LPS/LTA 1 mg/kg). Mice survival analysis curve and core temperature (T
) elevation curve were produced. NLRP3 inflammasome activation was measured by using real-time PCR and Western blot. Mice hypothalamus was dissected and neuroinflammation level was measured. To further demonstrate the role of NLRP3 inflammasome, Nlrp3 knockout mice were used. In addition, IL-1β neutralizing antibody was injected to test potential therapeutic effect on heat stroke.
Prior infection simulated by LPS/LTA injection resulted in latent inflammation status presented by high levels of cytokines in peripheral serum. However, LPS/LTA failed to cause any change in animal survival rate or body temperature. In the absence of LPS/LTA, heat treatment induced heat stroke and animal death without significant systemic or neuroinflammation. Despite a decreased level of IL-1β in hypothalamus, Nlrp3 knockout mice demonstrated no survival advantage under mere heat exposure. In animals with prior infection, their heat tolerance was severely impaired and NLRP3 inflammasome induced neuroinflammation was detected. The use of Nlrp3 knockout mice enhanced heat tolerance and alleviated heat stroke-induced death by reducing mice hypothalamus IL-1β production with prior infection condition. Furthermore, IL-1β neutralizing antibody injection significantly extended endotoxemic mice survival under heat stroke.
Based on the above results, NLRP3/IL-1β induced neuroinflammation might be an important mechanistic factor in heat stroke pathology, especially with prior infection. IL-1β may serve as a biomarker for heat stroke severity and potential therapeutic method.
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