A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.Comment: 72 page

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Robot manipulator has become an exciting topic for many researchers during several decades. They have investigated the advanced algorithms such as sliding mode control, neural network, or genetic scheme to implement these developments. However, they lacked the integration of these algorithms to explore many potential expansions. Simultaneously, the complicated system requires a lot of computational costs, which is not always supported. Therefore, this paper presents a novel design of switching mechanisms to control the robot manipulator. This investigation is expected to achieve superior performance by flexibly adjusting various strategies for better selection. The Proportional-Integral-Derivative (PID) scheme is well-known, easy to implement, and ensures rapid computation while it might not have much control effect. The advanced interval type-2 fuzzy sliding mode control properly deals with nonlinear factors and disturbances. Consequently, the PID scheme is switched when the tracking error is less than the threshold or is far from the target. Otherwise, the interval type-2 fuzzy sliding mode control scheme is activated to cope with unknown factors. The main contributions of this paper are (i) the recommendation of a suitable switching mechanism to drive the robot manipulator, (ii) the successful integration of the interval type-2 fuzzy sliding mode control to track the desired trajectory, and (iii) the launching of several tests to validate the proposed controller with robot model. From these achievements, it would be stated that the proposed approach is effective in tracking performance, robust in disturbance-rejection, and feasible in practical implementation

Đánh giá một số kỹ thuật phát hiện thư rác ứng dụng thuật toán xếp hạng người dùng trong mạng thư điện tử tại trường đại học Hà Nội

In this paper, four spam-filtering approaches based on user ranking in the mail networks: Clustering, Extended Clustering Coefficient, PageRank Algorithm and Weighted PageRank Algorithm are analyzed. We also propose a couple of fully worked-out datasets from the email network of Hanoi University against which the experimental comparisons with the respect to the accuracy of email user ranking and spam filtering are conducted. The results indicate that PageRank Algorithm and Extended Clustering Coefficient approaches are better than others. The rate of true detection is over 99.5%, while the failed alarm remains below 0.5%.Bài báo phân tích và kiểm nghiệm bốn phương pháp lọc thư rác dựa trên việc xếp hạng người dùng trong mạng thư điện tử: Phương pháp độ phân cụm, phương pháp độ phân cụm mở rộng, phương pháp sử dụng thuật toán PageRank và phương pháp sử dụng thuật toán PageRank có trọng số. Các thí nghiệm được thực hiện trên một số tập dữ liệu hoàn chỉnh của mạng thư điện tử Đại học Hà Nội. So sánh kết quả các thí nghiệm cho thấy, phương pháp sử dụng thuật toán PageRank và phương pháp độ phân cụm mở rộng mở rộng có kết quả tốt hơn các phương pháp còn lại. Tỷ lệ phát hiện thành công thư rác lên tới trên 99,5% trong khi tỷ lệ báo động nhầm thấp hơn 0,5%