Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game

Ant colony optimization (ACO) is prone to get into the local optimum and has a slow convergence speed when it is applied to the Travelling Salesman Problem (TSP). Therefore, for overcoming the drawbacks of ACO, a heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game is proposed. Firstly, three subpopulations with different characteristics are constructed into heterogeneous ant colony to enhance the performance of the ant colony. Secondly, the adaptive interactive learning mechanism is adopted when the algorithm diversity decreases, in which the objects to be communicated are selected adaptively according to the population similarity. In this mechanism, the way of communication is to pair the inferior individuals with the superior individuals, which enlarges the searching range and speeds up the convergence speed. Finally, an elite information exchange strategy based on non-zero-sum game is adopted when the algorithm falls into local optimum, in which each subpopulation selects the partners for elite information exchange according to the normalized comprehensive evaluation operator, which is helpful for each subpopulation to select the most appropriate strategy for getting out of the local optimal. Through this model, the accuracy of the solution is further improved. The data that used for this experiment is from the TSPLIB library under MATLAB simulation with various ranges of TSP datasets. Experimental results indicate that the proposed algorithm has a higher quality solution and faster convergence speed in solving the traveling salesman problem.

Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study

Background: Coronary artery disease distribution along the vessel is a main determinant of FFR improvement after PCI. Identifying focal from diffuse disease from visual inspections of coronary angiogram (CA) and FFR pullback (FFR-PB) are operator-dependent. Computer science may standardize interpretations of such curves.Methods: A virtual stenting algorithm (VSA) was developed to perform an automated FFR-PB curve analysis. A survey analysis of the evaluations of 39 vessels with intermediate disease on CA and a distal FFR <0.8, rated by 5 interventional cardiologists, was performed. Vessel disease distribution and PCI strategy were successively rated based on CA and distal FFR (CA); CA and FFR-PB curve (CA/FFR-PB); and CA and VSA (CA/VSA). Inter-rater reliability was assessed using Fleiss kappa and an agreement analysis of CA/VSA rating with both algorithmic and human evaluation (operator) was performed. We hypothesize that VSA would increase rater agreement in interpretation of epicardial disease distribution and subsequent evaluation of PCI eligibility.Results: Inter-rater reliability in vessel disease assessment by CA, CA/FFR-PB, and CA/VSA were respectively, 0.32 (95% CI: 0.17–0.47), 0.38 (95% CI: 0.23–0.53), and 0.4 (95% CI: 0.25–0.55). The raters' overall agreement in vessel disease distribution and PCI eligibility was higher with the VSA than with the operator (respectively, 67 vs. 42%, and 80 vs. 70%, both p < 0.05). Compared to CA/FFR-PB, CA/VSA induced more reclassification toward a focal disease (92 vs. 56.2%, p < 0.01) with a trend toward more reclassification as eligible for PCI (70.6 vs. 33%, p = 0.06). Change in PCI strategy did not differ between CA/FFR-PB and CA/VSA (23.6 vs. 28.5%, p = 0.38).Conclusions: VSA is a new program to facilitate and standardize the FFR pullback curves analysis. When expert reviewers integrate VSA data, their assessments are less variable which might help to standardize PCI eligibility and strategy evaluations.Clinical Trial Registration:https://www.clinicaltrials.gov/ct2/show/NCT03824600.

Region Growing Based Optical Fiber Panel Shadow Detection

Automatic defect detection of light image is very important in optical fiber panel (OFP) research now. In order to achieve detection of shadow defect automatically, proposed a new region growing algorithm. Using gray characteristics and fuzzy connectedness of image, realized automatic seeds selection by first selecting seed window then selecting seed points. Realized region growing algorithm with adaptive threshold by using maximum between-class variance method (OTSU). Proposed one OFP shadow detection evaluation operator, evaluation results showed that algorithm proposed in this paper was more accurate positioning of the shadow, achieved significant reduction in redundant information, and improved segmentation quality of the image effectively.