ALBERT

Description: In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.

Description: This paper proposes a new group-sparsity-inducing regularizer to approximate ℓ2,0 pseudo-norm. The regularizer is nonconvex, which can be seen as a linearly involved generalized Moreau enhancement of ℓ2,1-norm. Moreover, the overall convexity of the corresponding group-sparsity-regularized least squares problem can be achieved. The model can handle general group configurations such as weighted group sparse problems, and can be solved through a proximal splitting algorithm. Among the applications, considering that the bias of convex regularizer may lead to incorrect classification results especially for unbalanced training sets, we apply the proposed model to the (weighted) group sparse classification problem. The proposed classifier can use the label, similarity and locality information of samples. It also suppresses the bias of convex regularizer-based classifiers. Experimental results demonstrate that the proposed classifier improves the performance of convex ℓ2,1 regularizer-based methods, especially when the training data set is unbalanced. This paper enhances the potential applicability and effectiveness of using nonconvex regularizers in the frame of convex optimization.

Description: This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

Description: This paper investigated the flexible job-shop scheduling problem with the heat treatment process. To solve this problem, we built an unified mathematical model of the heat treatment process and machining process. Up to now, this problem has not been investigated much. Based on the features of this problem, we are intended to minimize Cmax, maximize the space utilization rate of heat treatment equipment, and minimize the total delay penalty to optimize the scheduling. By taking the dynamic process arrival under consideration, this paper proposed a set of decoding rules based on the heat treatment equipment volume and job delivery date to achieve a hybrid dynamic scheduling solution during one scheduling procedure. When the utilization rate of heat treatment equipment volume is maximized, and the job delivery date is taken under consideration, it is preferred to minimize the number of workpiece batches in the same job, and reduce the waiting time of the pending job. In combination with the improved adaptive non-dominated genetic algorithm, we worked out the solution. Furthermore, we verified the effectiveness of the proposed decoding rules and improved algorithm through algorithm comparison and calculation results. Finally, a software system for algorithm verification and algorithm comparison was developed to verify the validity of our proposed algorithm.

Description: The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.

Description: The article presents a parallel hardware-oriented algorithm designed to speed up the division of two octonions. The advantage of the proposed algorithm is that the number of real multiplications is halved as compared to the naive method for implementing this operation. In the synthesis of the discussed algorithm, the matrix representation of this operation was used, which allows us to present the division of octonions by means of a vector–matrix product. Taking into account a specific structure of the matrix multiplicand allows for reducing the number of real multiplications necessary for the execution of the octonion division procedure.

Description: Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Description: The computer information service industry is closely related to the fourth industrial revolution and stands at the core of the global value chain. It has become an essential engine for developing industries in various countries, and its scale is constantly expanding. In the critical period of global economic transformation and development, the use of mathematical models to predict its international competitiveness will help scientifically evaluate the development level of the industry and accelerate the adaptation to the needs of the fourth industrial revolution. In this article, a prediction model is proposed for the international competitiveness of the computer information service industry. First, we used the Revealed Comparative Advantage (RCA) index to measure the international competitiveness of the computer information service industry. Furthermore, based on the characteristics of the industry and high-quality development theory, we constructed the evaluation indicator system of influencing factors and used the grey relational analysis method to screen key indicators. Then, we combined the Grey model and BP neural network algorithm to construct the GM-BP prediction model. Finally, China is used as an example to predict the international competitiveness of its computer information service industry, and suggestions are made for industrial development. The results show that the grey relational analysis method can genuinely reflect the impact of different aspects on the international competitiveness of China’s computer information service industry and better determine the key indicators of influencing factors. The GM-BP model has minor errors and excellent simulation results and can accurately predict the future status of international competitiveness. The applicability and reliability of the model are reasonable.

Description: The problem of efficiently cutting smaller two-dimensional pieces from a larger surface is recurrent in several manufacturing settings. This problem belongs to the domain of cutting and packing (C&P) problems. This study approached a category of C&P problems called the minimum time cut path (MTCP) problem, which aims to identify a sequence of cutting and sliding movements for the head device to minimize manufacturing time. Both cutting and slide speeds (just moving the head) vary according to equipment, despite their relevance in real-world scenarios. This study applied the MTCP problem on the practical scope and presents two metaheuristics for tackling more significant instances that resemble real-world requirements. The experiments presented in this study utilized parameter values from typical laser cutting machines to assess the feasibility of the proposed methods compared to existing commercial software. The results show that metaheuristic-based solutions are competitive when addressing practical problems, achieving increased performance regarding the processing time for 94% of the instances.

Description: The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time.