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Reinforcement learning is one of the promising approaches for algorithmic trading in financial markets. However, in certain situations, buy or sell orders issued by an algorithmic trading program may not be fulfilled entirely. By considering the actual scenarios from the financial markets, in this paper, we propose a novel framework named Supervised Actor-Critic Reinforcement Learning with Action Feedback (SACRL-AF) for solving this problem. The action feedback mechanism of SACRL-AF notifies the actor about the dealt positions and corrects the transitions of the replay buffer. Meanwhile, the dealt positions are used as the labels for the supervised learning. Recent studies have shown that Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) are more stable and superior to other actor-critic algorithms. Against this background, based on the proposed SACRL-AF framework, two reinforcement learning algorithms henceforth referred to as Supervised Deep Deterministic Policy Gradient with Action Feedback (SDDPG-AF) and Supervised Twin Delayed Deep Deterministic Policy Gradient with Action Feedback (STD3-AF) are proposed in this paper. Experimental results show that SDDPG-AF and STD3-AF achieve the state-of-art performance in profitability. [ABSTRACT FROM AUTHOR]

Ransomware-as-a-service (RaaS) is increasing the scale and complexity of ransomware attacks. Understanding the internal operations behind RaaS has been a challenge due to the illegality of such activities. The recent chat leak of the Conti RaaS operator, one of the most infamous ransomware operators on the international scene, offers a key opportunity to better understand the inner workings of such organizations. This paper analyzes the main discussion topics in the Conti chat leak using machine learning techniques such as Natural Language Processing (NLP) and Latent Dirichlet Allocation (LDA), as well as visualization strategies. Five discussion topics are found: (1) Business, (2) Technical, (3) Internal tasking/Management, (4) Malware, and (5) Customer Service/Problem Solving. Moreover, the distribution of topics among Conti members shows that only 4% of individuals have specialized discussions while almost all individuals (96%) are all-rounders, meaning that their discussions revolve around the five topics. The results also indicate that a significant proportion of Conti discussions are non-tech related. This study thus highlights that running such large RaaS operations requires a workforce skilled beyond technical abilities, with individuals involved in various tasks, from management to customer service or problem solving. The discussion topics also show that the organization behind the Conti RaaS operator shares similarities with a large firm. We conclude that, although RaaS represents an example of specialization in the cybercrime industry, only a few members are specialized in one topic, while the rest runs and coordinates the RaaS operation. [ABSTRACT FROM AUTHOR]

With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of "tribo-informatics" as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of "AI for tribology". In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology. [ABSTRACT FROM AUTHOR]

Within the federated learning (FL) framework, the client collaboratively trains the model in coordination with a central server, while the training data can be kept locally on the client. Thus, the FL framework mitigates the privacy disclosure and costs related to conventional centralized machine learning. Nevertheless, current surveys indicate that FL still has problems in terms of communication efficiency and privacy risks. In this paper, to solve these problems, we develop an FL framework with communication-efficient and privacy-preserving (FLCP). To realize the FLCP, we design a novel compression algorithm with efficient communication, namely, adaptive weight compression FedAvg (AWC-FedAvg). On the basis of the non-independent and identically distributed (non-IID) and unbalanced data distribution in FL, a specific compression rate is provided for each client, and homomorphic encryption (HE) and differential privacy (DP) are integrated to provide demonstrable privacy protection and maintain the desirability of the model. Therefore, our proposed FLCP smoothly balances communication efficiency and privacy risks, and we prove its security against "honest-but-curious" servers and extreme collusion under the defined threat model. We evaluate the scheme by comparing it with state-of-the-art results on the MNIST and CIFAR-10 datasets. The results show that the FLCP performs better in terms of training efficiency and model accuracy than the baseline method. [ABSTRACT FROM AUTHOR]

Minimizing the level of material consumption in textile production is a major concern. The cornerstone of this optimization task is the nesting problem, whose goal is to lay a set of irregular 2D parts out onto a rectangular surface, called the nesting zone, while respecting a set of constraints. Knowing the efficiency—ratio of usable to used up material enables the optimization of several textile production problems. Unfortunately, knowing the efficiency requires the nesting problem to be solved, which is computationally intensive and has been proven to be NP-hard. This paper introduces a regression approach to estimate efficiency without solving the nesting problem. Our approach models the 2D nesting problem as a graph where the nodes are images derived from parts and the edges hold the constraints. The method then consists of combining convolutional neural networks for addressing the image-based aspects and graph neural networks (GNNs) for the constraint aspects. We evaluate several neural message passing approaches on our dataset and obtain results that are sufficiently accurate for enabling several business use cases, where our model best solves this task with a mean absolute error of 1.65. We provide open access to our dataset, whose properties differ from those of other graph datasets found in the literature. This dataset is constructed on 100,000 real customers' nesting data. Along the way, we compare the performance and generalization capabilities of four GNN architectures obtained from the literature on this dataset. [ABSTRACT FROM AUTHOR]

Since 2007, methods that utilize side-channel data to detect hardware Trojan (HT) problems have been widely studied. Machine learning methods are widely used for hardware Trojan detection, but with the development of integrated circuits (ICs), better results are usually obtained using deep learning methods. In this paper, we propose an architecture inspired by Residual-Block and Dense-Block and combine it with SE Attention Mechanism, which we named the Res-Dense-SE-Net network. By combining residual connectivity, dense connectivity, and attention mechanism, the Res-Dense-SE-Net network can enjoy the advantages of these three network architectures at the same time, which can improve the expressiveness and performance of the model. The Res-Dense-SE-Net network can capture the key features in the image better, and it can solve the problems of gradient vanishing and feature transfer efficiently, which can in turn improve the classification accuracy and the generalization ability of the model. Based on the publicly available AES series of hardware Trojans from TrustHub and the publicly available hardware Trojan-side channel data by Faezi et al., we evaluate the effectiveness of the method proposed in this paper. The experimental results show that when a single Trojan exists, the method proposed in this paper has a high accuracy rate; and when multiple types of hardware Trojans exist at the same time and need to be categorized, the categories of hardware Trojans can also be effectively identified, and the categorization accuracy is high compared with the existing deep learning methods. [ABSTRACT FROM AUTHOR]

Online portfolio optimization with transaction costs is a big challenge in large-scale intelligent computing community, since its undersample from rapidly-changing market and complexity from varying transaction costs. In this paper, we focus on this problem and solve it by machine learning system. Specifically, we reformulate the optimization problem with the minimization over simplex containing three items, which are negative expected return, the elastic net regularization of transaction costs controlled term and portfolio variable, respectively. We propose to apply linearized augmented Lagrangian method (LALM) and the alternating direction method of multipliers (ADMM) to solve the optimization model in a higher efficiency, meanwhile theoretically guarantee their convergence and deduce closed-form solutions of their subproblems in each iteration. Furthermore, we conduct extensive experiments on five benchmark datasets from real market to demonstrate that the proposed algorithms outperform compared state-of-the-art strategies in most cases in six dimensions. [ABSTRACT FROM AUTHOR]

A community is composed of closely related nodes. Detecting communities in a network has many practical applications, such as online product recommendation, biological molecule discovery and criminal group tracking. In recent years, network representation learning (NRL) has attracted much attention in the field of community detection because it can effectively extract complex relations between nodes which improves the quality of detected communities. In many real-world networks, the rich attribute information contained in nodes and the similarity between nodes and their multi-order neighbors has significant contributions to the generation of node embedding vectors in NRL. However, existing NRL algorithms treat a node's different order neighbors equally as high-order contexts, leading to the ignorance of their different impacts on the generation of the node's embedding vector. In addition, these algorithms do not focus on the coupling and interaction relations between nodes playing similar structural roles, which may ignore some nodes in a community with similar structural roles. In this paper, we propose a novel autoencoder considering the multi-order similarity and structural role similarity (AMOSOS) to solve the above problems. First, we design a strategy to obtain a multi-order weight matrix which preserves the differential influence of neighbors of different orders by sequentially decreasing the weight of each order. Second, we design a role similarity indicator to capture the complex coupling and interaction relations of nodes in the network. Experimental results on synthetic networks and real-world networks show that our proposed algorithm is more accurate than existing network representation learning algorithms for the task of community detection. [ABSTRACT FROM AUTHOR]

In the development of anti-breast cancer drugs, the quantitative structure-activity relationship model of compounds is usually used to select potential active compounds. However, the existing methods often have problems such as low model prediction performance, lack of overall consideration of the biological activity and related properties of compounds, and difficulty in directly selection candidate drugs. Therefore, this paper constructs a complete set of compound selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization problem solving. In feature selection part, a feature selection method based on unsupervised spectral clustering is proposed. The selected features have more comprehensive information expression ability. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected to perform the relationship mapping between each other, and better prediction performance is achieved. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved and used to solve this problem. Compared with various algorithms, the improved algorithm has better search performance. [ABSTRACT FROM AUTHOR]

The paper deals with the construction of interpretable machine learning models. The approximation problem is solved for a set of shapes on a contour image. Assumptions that the shapes are second-order curves are introduced. When approximating the shapes, information about the type, location, and shape of curves as well as about the set of their possible transformations is used. Such information is called expert information, and the machine learning method based on expert information is called expert-augmented learning. It is assumed that the set of shapes is approximated by the set of local models. Each local model based on expert information approximates one shape on the contour image. To construct the models, it is proposed to map second-order curves into a feature space in which each local model is linear. Thus, second-order curves are approximated by a set of linear models. In a computational experiment, the problem of approximating an iris on a contour image is considered. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem that minimizing the sum of a nonsmooth function with a smooth one in the nonconvex setting, which arising in many contemporary applications such as machine learning, statistics, and signal/image processing. To solve this problem, we propose a new nonmonotone accelerated proximal gradient method with variable stepsize strategy. Note that incorporating inertial term into proximal gradient method is a simple and efficient acceleration technique, while the descent property of the proximal gradient algorithm will lost. In our algorithm, the iterates generated by inertial proximal gradient scheme are accepted when the objective function values decrease or increase appropriately; otherwise, the iteration point is generated by proximal gradient scheme, which makes the function values on a subset of iterates are decreasing. We also introduce a variable stepsize strategy, which does not need a line search or does not need to know the Lipschitz constant and makes the algorithm easy to implement. We show that the sequence of iterates generated by the algorithm converges to a critical point of the objective function. Further, under the assumption that the objective function satisfies the Kurdyka–Łojasiewicz inequality, we prove the convergence rates of the objective function values and the iterates. Moreover, numerical results on both convex and nonconvex problems are reported to demonstrate the effectiveness and superiority of the proposed method and stepsize strategy. [ABSTRACT FROM AUTHOR]

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

The use of predictive systems has become wider with the development of related computational methods, and the evolution of the sciences in which these methods are applied Solon and Selbst (Calif L REV 104: 671–732, 2016) and Pedreschi et al. (2007). The referred methods include machine learning techniques, face and/or voice recognition, temperature mapping, and other, within the artificial intelligence domain. These techniques are being applied to solve problems in socially and politically sensitive areas such as crime prevention and justice management, crowd management, and emotion analysis, just to mention a few. However, dissimilar predictions can be found nowadays as the result of the application of these methods resulting in misclassification, for example for the case of conviction risk assessment Office of Probation and Pretrial Services (2011) or decision-making process when designing public policies Lange (2015). The goal of this paper is to identify current gaps on fairness achievement within the context of predictive systems in artificial intelligence by analyzing available academic and scientific literature up to 2020. To achieve this goal, we have gathered available materials at the Web of Science and Scopus from last 5 years and analyzed the different proposed methods and their results in relation to the bias as an emergent issue in the Artificial Intelligence field of study. Our tentative conclusions indicate that machine learning has some intrinsic limitations which are leading to automate the bias when designing predictive algorithms. Consequently, other methods should be explored; or we should redefine the way current machine learning approaches are being used when building decision making/decision support systems for crucial institutions of our political systems such as the judicial system, just to mention one. [ABSTRACT FROM AUTHOR]

We consider a class of problems where the objective function is the sum of a smooth function and a composition of nonconvex and nonsmooth functions. Such optimization problems arise frequently in machine learning and data processing. The proximal iteratively reweighted method has been widely used and popularized in solving these problems. In this paper, we develop an extrapolated proximal iteratively reweighted method that incorporates two different flexible inertial steps at each iteration. We first prove the subsequential convergence of the proposed method under parameter constraints. Moreover, if the objective function satisfies the Kurdyka-Łojasiewicz property, the global convergence of the new method is established. In addition, we analyze the local convergence rate by making assumptions on the Kurdyka-Łojasiewicz exponent of the objective function. Finally, numerical results on l p minimization and feature selection problems are reported to show the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. [ABSTRACT FROM AUTHOR]

With unlabeled and high-dimensional data explosion, unsupervised feature selection has become an essential step in many machine learning and data mining tasks. Many dictionary learning based models have been successfully developed for unsupervised feature selection in recent years. These models learn an over-complete dictionary to investigate more data distribution information. However, over-complete dictionary learning will generate redundancy in the latent representations for data. Moreover, if data contain noise, dictionary learning will also yield noise in the latent representations. In this paper, we propose a novel unsupervised feature selection framework, named dictionary learning for unsupervised feature selection via dual sparse regression. In this model, dictionary learning is first embedded into a sparse regression to learn an over-complete dictionary with sparse representations for data, in which the redundancy and noise are eliminated. The data are then projected to the representations to evaluate the significance of features using the other sparse regression. We also offer an efficient algorithm to solve this problem and theoretically analyze its convergence and computational complexity, which is proportional to the data dimensionality. Finally, the evaluation results with the k-means task utilizing the selected features on 9 benchmark datasets demonstrate the superiority of our approach in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Machine learning is being increasingly used to make algorithmic decisions that have strong societal impact on people's lives. Due to their huge societal impact, such algorithmic decisions need to be accurate and fair with respect to sensitive features, including race, gender, religion, and sexual orientation. To achieve a good balance between prediction accuracy and fairness, causality-based methods have been proposed, which utilize a causal graph with unfair pathways. However, none of these methods can ensure fairness for each individual without making restrictive functional assumptions about the data generating processes, which are not satisfied in many cases. In this paper, we propose a far more practical causality-based framework for learning an individually fair classifier. To avoid impractical functional assumptions, we introduce a new criterion, the probability of individual unfairness, and derive its upper bound that can be estimated from data. We then train a classifier by solving an optimization problem where the upper bound value is forced to be close to zero. We elucidate why solving such an optimization problem can guarantee fairness for each individual. Moreover, we provide two extensions for dealing with challenging real-world scenarios where there are unobserved variables called latent confounders, and the true causal graph is uncertain. Experimental results show that our method can learn an individually fair classifier at a slight cost of prediction accuracy. [ABSTRACT FROM AUTHOR]

When solving programming problems with objectives, we are often faced with the challenge of insufficient samples. And when new samples are generated, re-modeling based on historical and incremental samples is costly. Many Bayesian based approaches have been proposed to update the original models using newly coming samples which are called incremental learning. In this paper, we derive a calculation method of information value based on Bayesian quadrature technology, and use multi-start Adaptive Momentum (ADAM) stochastic gradient algorithm to design independent adaptive learning rates by computing the first-order moment and the second-order moment of the gradient then we propose a small-sample size problems solving based on incremental learning called Adaptive Bayesian Quadrature Approach (ABQA) We use a numerical validation experiment to verify the effectiveness of our approach, and then use two programming problems, shared bike system and newsvendor problem, to verify the effectiveness of our approach on incremental learning and small-samples size problems. In all these experiments, ABQA outperforms the benchmarks and state-of-the-art Bayesian algorithms. Performance and speed are improved by 2.8% and 8.6%, respectively. [ABSTRACT FROM AUTHOR]

Dictionary learning has become a research hotspot. How to construct a robust dictionary is a key issue. In face recognition problem, differences in expressions, postures, and lighting conditions are key factors that affect the accuracy. Therefore, images of the same face can be very different in different situations. In real-world scenario, the samples of each face are very limited, which make it hard for the network to generalize well. Therefore, To solve the problem mentioned above, this paper proposes a method to construct a robust dictionary. In the method, virtual samples are generated to appropriately reflect the diversity of the face images, and based on this, two dictionaries are constructed and a corresponding fusion classification scheme is designed. The main advantages of this method are as follows: firstly, the simultaneous use of virtual samples and original samples can better reflect the facial appearance of each morphology, and the dictionaries obtained help to improve the robustness of the dictionary learning algorithm. Secondly, the proposed fusion classification scheme can give full play to the performance of the double dictionary learning algorithm. The results of out experiments show that the proposed algorithm is superior to some existing algorithms. [ABSTRACT FROM AUTHOR]

The task of extracting entities and relations has evolved from distributed extraction to joint extraction. The joint model overcomes the disadvantages of distributed extraction method and strengthens the information interaction between entities and relations. However, the existing methods of the joint model rarely pay attention to the semantic information between words, which have limitations in solving the problem of overlapping relations. In this paper, we propose an RMAN model for joint extraction of entities and relations, which includes multi-feature fusion encoder sentence representation and decoder sequence annotation. We first add a multi-head attention layer after Bi-LSTM to obtain sentence representations, and leverage the attention mechanism to capture relation-based sentence representations. Then, we perform sequence annotation on the sentence representation to obtain entity pairs. Experiments on NYT-single, NYT-multi and WebNLG datasets demonstrate that our model can efficiently extract overlapping triples, which outperforms other baselines. [ABSTRACT FROM AUTHOR]

Path planning of intelligent driving vehicles in emergencies is a hot research issue, this paper proposes a new method of the best path selection for the intelligent driving vehicles to solve this problem. Based on the prior knowledge applied reinforcement learning strategy and the searching- optimized A* algorithm, we designed a hybrid algorithm to help intelligent driving vehicles selecting the best path in the traffic network in emergencies including limited height, width, weight, accident, and traffic jam. Through simulation experiments and scene experiments, it is proved that the proposed algorithm has good stability, high efficiency, and practicability. [ABSTRACT FROM AUTHOR]

The learning with errors (LWE) problem is a problem derived from machine learning that is believed to be intractable for quantum computers. This paper proposes a method that can reduce an LWE problem to a set of maximum independent set (MIS) problems, which are graph problems that are suitable for a quantum annealing machine to solve. The reduction algorithm can reduce an n-dimensional LWE problem to several small MIS problems with at most 2 O (n) nodes when the lattice-reduction algorithm used in the LWE-reduction method successfully finds short vectors. The algorithm is useful for solving LWE problems in a quantum-classical hybrid manner by using an existing quantum algorithm to solve the MIS problems. For example, the smallest LWE challenge problem is reduced to MIS problems with about 40,000 vertices. This result means that the smallest LWE challenge problem will be within the scope of a real quantum computer in the near future. [ABSTRACT FROM AUTHOR]

A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to 2.8 × speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines. [ABSTRACT FROM AUTHOR]

The power quality and efficiency of the hydro-power station depend on the stable operation of the hydro-generator unit, which needs to continue to operate and it is prone to axis failure. Therefore, to adopt effective axis adjustment technology to eliminate faults. This paper proposes a new method for axis adjustment of hydro-generator unit based on an improved grey prediction model and swarms intelligence optimization neural network. First of all, it proposes a sequence acceleration translation and mean value transformation method, which is used to pre-process the axis net total swing sequence that exhibits oscillating fluctuations. It uses e1 and e2 factor transformation to establish an improved axis net total swing gray prediction model. Then, the advanced flamingo search algorithm is used to search the maximum value of the sine function of the net total pendulum of the axis, and the axis adjustment orientation is obtained. This method solves the problem that GM(1, 1) can only be predicted by monotone sequence in the past and the problem that the search algorithm is easy to fall into local optimum, effectively improves the calculation efficiency of axis and shorts the search time. Simulation examples show that the proposed method can significantly improve accuracy of axis adjustment. This method greatly improves the efficiency of azimuth search for axis adjustment. [ABSTRACT FROM AUTHOR]

Oversampling is an effective method to fulfill imbalanced learning, owing to its easy-to-go capability of achieving the balance by synthesizing new samples. However, precise synthesizing in oversampling is always a significant yet challenging task due primarily to various problems such as noise samples, within-class imbalance, and selection of boundary samples. In order to solve these problems, this paper proposes a new improved oversampling method, called adaptively weighted three-way decision oversampling (AWTDO) for imbalanced learning. The working principle of the proposed AWTDO method includes three main steps. Firstly, remove the noise sample roughly, implement K-means clustering algorithm on raw data to establish multi-clusters, and calculate imbalanced ratio of each cluster. Secondly, classify all clusters into three categories according to their imbalanced ratios and three-way decision, such as positive domain, boundary domain, and negative domain. Accordingly, assign the number of synthetic samples distinguishably to each cluster regarding its category. Thirdly, determinatively select the target minority sample in each cluster and generate the new synthetic samples by using the stochastic linear interpolation technique according to different sampling weight. Finally, some comparative experiments on public datasets have shown that the proposed AWTDO method outperforms nine state-of-the-art oversampling methods. [ABSTRACT FROM AUTHOR]

Monocular depth estimation and semantic segmentation are two fundamental goals of scene understanding. Due to the advantages of task interaction, many works have studied the joint-task learning algorithm. However, most existing methods fail to fully leverage the semantic labels, ignoring the provided context structures and only using them to supervise the prediction of segmentation split, which limits the performance of both tasks. In this paper, we propose a network injected with contextual information (CI-Net) to solve this problem. Specifically, we introduce a self-attention block in the encoder to generate an attention map. With supervision from the ideal attention map created by semantic label, the network is embedded with contextual information so that it could understand the scene better and utilize correlated features to make accurate prediction. Besides, a feature-sharing module (FSM) is constructed to make the task-specific features deeply fused, and a consistency loss is devised to ensure that the features mutually guided. We extensively evaluate the proposed CI-Net on NYU-Depth-v2, SUN-RGBD, and Cityscapes datasets. The experimental results validate that our proposed CI-Net could effectively improve the accuracy of semantic segmentation and depth estimation. [ABSTRACT FROM AUTHOR]

In close-range air combat, highly reliable trajectory prediction results can help pilots to win victory to a great extent. However, traditional trajectory prediction methods can only predict the precise location that the target aircraft may reach, which cannot meet the requirements of high-precision, real-time trajectory prediction for highly maneuvering targets. To this end, this paper proposes an attention-based convolution long sort-term memory (AttConvLSTM) network to calculate the arrival probability of each space in the reachable area of the target aircraft. More specifically, by segmenting the reachable area, the trajectory prediction problem is transformed into a classification problem for solution. Second, the AttConvLSTM network is proposed as an efficient feature extraction method, and combined with the multi-layer perceptron (MLP) to solve this classification problem. Third, a novel loss function is designed to accelerate the convergence of the proposed model. Finally, the flight trajectories generated by experienced pilots are used to evaluate the proposed method. The results indicate that the mean absolute error of the proposed method is no more than 45.73m, which is of higher accuracy compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

One of the crucial challenges of solving many-objective optimization problems is uniformly well covering of the Pareto-front (PF). However, many the state-of-the-art optimization algorithms are capable of approximating the shape of many-objective PF by generating a limited number of non-dominated solutions. The exponential increase of the population size is an inefficient strategy that increases the computational complexity of the algorithm dramatically—especially when solving many-objective problems. In this paper, we introduce a machine learning-based framework to cover sparse PF surface which is initially generated by many-objective optimization algorithms; either by classical or meta-heuristic methods. The proposed method, called many-objective reverse mapping (MORM), is based on constructing a learning model on the initial PF set as the training data to reversely map the objective values to corresponding decision variables. Using the trained model, a set of candidate solutions can be generated by a variety of inexpensive generative techniques such as Opposition-based Learning and Latin Hypercube Sampling in both objective and decision spaces. Iteratively generated non-dominated candidate solutions cover the initial PF efficiently with no further need to utilize any optimization algorithm. We validate the proposed framework using a set of well-known many-objective optimization benchmarks and two well-known real-world problems. The coverage of PF is illustrated and numerically compared with the state-of-the-art many-objective algorithms. The statistical tests conducted on comparison measures such as HV, IGD, and the contribution ratio on the built PF reveal that the proposed collaborative framework surpasses the competitors on most of the problems. In addition, MORM covers the PF effectively compared to other methods even with the aid of large population size. [ABSTRACT FROM AUTHOR]

The availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of quantum machine learning algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning practitioners to use the QDataSet to solve problems in applied quantum computation, such as quantum control, quantum spectroscopy and tomography. Accompanying the datasets on the associated GitHub repository are a set of workbooks demonstrating the use of the QDataSet in a range of optimisation contexts. Measurement(s) Simulations of one- and two-qubit quantum systems evolving in the presence and absence of noise and distortion Technology Type(s) Simulated measurement using Python packages Sample Characteristic - Organism Simulated quantum systems Sample Characteristic - Environment Quantum systems in noisy and noiseless environments [ABSTRACT FROM AUTHOR]

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper, we study this problem for finite-state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input–output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. We also carry out a performance comparison against two existing tools (LearnLib and flexfringe). Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample. [ABSTRACT FROM AUTHOR]

The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a "black box problem." That is, machine learning algorithms are "opaque" to human users, failing to be "interpretable" or "explicable" in terms that would render categorization procedures "understandable." The purpose of this paper is to challenge the widespread agreement about the existence and importance of a black box problem. The first section argues that "interpretability" and cognates lack precise meanings when applied to algorithms. This makes the concepts difficult to use when trying to solve the problems that have motivated the call for interpretability (etc.). Furthermore, since there is no adequate account of the concepts themselves, it is not possible to assess whether particular technical features supply formal definitions of those concepts. The second section argues that there are ways of being a responsible user of these algorithms that do not require interpretability (etc.). In many cases in which a black box problem is cited, interpretability is a means to a further end such as justification or non-discrimination. Since addressing these problems need not involve something that looks like an "interpretation" (etc.) of an algorithm, the focus on interpretability artificially constrains the solution space by characterizing one possible solution as the problem itself. Where possible, discussion should be reformulated in terms of the ends of interpretability. [ABSTRACT FROM AUTHOR]

Matrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size n × m , to a product WS where we require that S, a matrix of size n × k , needs to have consecutive ones property. More specifically, we require that each row of S needs to be in the form of 0 , ... , 0 , 1 , ... , 1 , 0 , ... , 0 . Such decompositions are particularly meaningful if X is a matrix where each row represents a time series; in such a case the ones in each row in S represent a time segment. We show that the optimization problem is inapproximable. To solve the problem we propose 5 different algorithms. The first two algorithms are based on solving iteratively S while keeping W fixed and then solving W while keeping S fixed. The next two algorithms are based on greedily optimizing a single row in S and the corresponding column in W. The last algorithm first finds the optimal decomposition of with 2 k - 1 non-overlapping rows, and then greedily combines the rows until k rows remain. We compare the algorithms experimentally, focusing on the quality of the decomposition as well as the computational time. We show experimentally that our algorithms yield interpretable results in practical time. [ABSTRACT FROM AUTHOR]

Traditional supervised learning algorithms do not satisfactorily solve the classification problem on imbalanced data sets, since they tend to assign the majority class, to the detriment of the minority class classification. In this paper, we introduce the Bayesian network-based over-sampling method (BOSME), which is a new over-sampling methodology based on Bayesian networks. Over-sampling methods handle imbalanced data by generating synthetic minority instances, with the benefit that classifiers learned from a more balanced data set have a better ability to predict the minority class. What makes BOSME different is that it relies on a new approach, generating artificial instances of the minority class following the probability distribution of a Bayesian network that is learned from the original minority classes by likelihood maximization. We compare BOSME with the benchmark synthetic minority over-sampling technique (SMOTE) through a series of experiments in the context of indirect cost-sensitive learning, with some state-of-the-art classifiers and various data sets, showing statistical evidence in favor of BOSME, with respect to the expected (misclassification) cost. [ABSTRACT FROM AUTHOR]

Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imbalanced classes is one of the most important issues in pattern recognition and machine learning. In this paper, a method is proposed in the form of a cost-sensitive optimization problem which implements the process of selecting and extracting the features simultaneously. The feature extraction phase is based on reducing error and maintaining geometric relationships between data by solving a manifold learning optimization problem. In the feature selection phase, the cost-sensitive optimization problem is adopted based on minimizing the upper limit of the generalization error. Finally, the optimization problem which is constituted from the above two problems is solved by adding a cost-sensitive term to create a balance between classes without manipulating the data. To evaluate the results of the feature reduction, the multi-class linear SVM classifier is used on the reduced data. The proposed method is compared with some other approaches on 21 datasets from the UCI learning repository, microarrays and high-dimensional datasets, as well as imbalanced datasets from the KEEL repository. The results indicate the significant efficiency of the proposed method compared to some similar approaches. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of open shop scheduling on two machines with resources constraints. In the context of our study, in order to be executed, a job requires first, for its preparation for a given period of time, a number of resources which cannot exceed a given resource capacity. Then, it goes onto its execution while the resources allocated to it become available again. We seek a schedule that minimizes the makespan. We first prove the $\mathcal{N}\mathcal{P}$ -hardness of several versions of this problem. Then, we present a well solvable case, lower bounds, and heuristic algorithms along with an experimental study. [ABSTRACT FROM AUTHOR]

This paper studies two single-machine scheduling problems with the effect of deterioration and learning. In this model, the processing times of jobs are defined as functions of their starting times and positions in a sequence. For the following two objective functions: the weighted sum of completion times and the maximum lateness, this paper proposes two heuristics according to the corresponding single machine problems without learning effect. This paper also gives the worst-case error bound for the heuristics and provides computational results to evaluate the performance of the heuristics. [ABSTRACT FROM AUTHOR]

Bi-Level Optimization Problem (BLOP) is a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem, which has another optimization problem as a constraint. In this way, the evaluation of each upper level solution requires finding an optimal solution to the corresponding lower level problem, which is computationally so expensive. For this reason, most proposed bi-level resolution methods have been restricted to solve the simplest case (linear continuous BLOPs). This fact has attracted the evolutionary computation community to solve such complex problems. Besides, to enhance the search performance of Evolutionary Algorithms (EAs), reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and was demonstrated much promise. Motivated by this observation, we propose in this paper, a memetic version of our proposed Co-evolutionary Decomposition-based Algorithm-II (CODBA-II), that we named M-CODBA-II, to solve combinatorial BLOPs. The main motivation of this paper is to incorporate transfer learning within our recently proposed CODBA-II scheme to make the search process more effective and more efficient. Our proposed hybrid algorithm is investigated on two bi-level production-distribution problems in supply chain management formulated to: (1) Bi-CVRP and (2) Bi-MDVRP. The experimental results reveal a potential advantage of memes incorporation in CODBA-II. Most notably, the results emphasize that transfer learning allows not only accelerating the convergence but also finding better solutions. [ABSTRACT FROM AUTHOR]

A class imbalance problem occurs when a dataset is decomposed into one majority class and one minority class. This problem is critical in the machine learning domains because it induces bias in training machine learning models. One popular method to solve this problem is using a sampling technique to balance the class distribution by either under-sampling the majority class or over-sampling the minority class. So far, diverse over-sampling techniques have suffered from overfitting and noisy data generation problems. In this paper, we propose an over-sampling scheme based on the borderline class and conditional generative adversarial network (CGAN). More specifically, we define a borderline class based on the minority class data near the majority class. Then, we generate data for the borderline class using the CGAN for data balancing. To demonstrate the performance of the proposed scheme, we conducted various experiments on diverse imbalanced datasets. We report some of the results. [ABSTRACT FROM AUTHOR]

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complicated calculation and large counter error of curve fitting method in industrial processing of noncircular glass ornaments, this paper proposes a grinding point searching algorithm. The successive approximation is used to find the grinding point of the workpiece contour and the grinding wheel. The proposed algorithm does not require complicated formula derivation and calculation and has good counter accuracy. According to the discrete characteristics of point-cloud data, a discrete cross-coupling iterative learning control algorithm is proposed to suppress the contour processing control error. Based on the norm theory, the convergence analysis and convergence conditions of the algorithm are presented. The effectiveness of the proposed algorithms is verified by experiments. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of direction of arrival (DOA) estimation with coprime array in the context of sparse signal reconstruction to fully exploit the enhanced degrees of freedom (DOF) offered by the difference coarray. The proposed method is based on the framework of sparse Bayesian learning and can jointly refine the unknown DOAs and the sparse signals in a gradual and interweaved manner. Specifically, the proposed approach is constructed by iteratively decreasing a surrogate function majorizing a given objective function, which results in accelerating the speed to converge to the global minimum. Furthermore, for facilitating a noise-free sparse representation, a customized linear transformation is judiciously incorporated in our sparsity-inducing DOA estimator to eliminate the unknown noise variance, and in the mean time, the sample covariance matrix perturbation can be normalized to an identity matrix as a by-product. Extensive simulation experiments under different conditions finally demonstrate the superiority of our suggested algorithm in terms of mean-squared DOA estimation error, DOF and resolution ability over state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the design of an exact exponential time algorithm with a proved worst-case running time for 3-machine flowshop scheduling problems considering worst-case scenarios. For the minimization of the makespan criterion, a Dynamic Programming algorithm running in O∗(3n) is proposed, which improves the current best-known time complexity 2O(n)×‖I‖O(1) in the literature. The idea is based on a dominance condition and the consideration of the Pareto Front in the criteria space. The algorithm can be easily generalized to other problems that have similar structures. The generalization on two problems, namely the F3‖fmax and F3‖∑fi problems, is discussed. [ABSTRACT FROM AUTHOR]

Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. [ABSTRACT FROM AUTHOR]

The solution of intractable problems implies the use of heuristics. Quantum computers may find use for optimization problems, but have yet to solve any NP-hard problems. This paper demonstrates results in game theory for domain transference and the reuse of problem-solving knowledge through the application of learned heuristics. It goes on to explore the possibilities for the acquisition of heuristics for the solution of the NP-hard TSP problem. Here, it is found that simple heuristics (e.g., pairwise exchange) often work best in the context of more or less sophisticated experimental designs. Often, these problems are not amenable to exclusive logic solutions; but rather, require the application of hybrid approaches predicated on search. In general, such approaches are based on randomization and supported by parallel processing. This means that heuristic solutions emerge from attempts to randomize the search space. The paper goes on to present a constructive proof of the unbounded density of knowledge in support of the Semantic Randomization Theorem (SRT). It highlights this result and its potential impact upon the community of machine learning researchers. [ABSTRACT FROM AUTHOR]

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in scenarios where computational power is restricted. We consider in this paper a simple yet efficient alternative to sparse coding for feature extraction. We study a classification scheme that applies the soft-thresholding nonlinear mapping in a dictionary, followed by a linear classifier. A novel supervised dictionary learning algorithm tailored for this low complexity classification architecture is proposed. The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver. We conduct experiments on several datasets, and show that our learning algorithm that leverages the structure of the classification problem outperforms generic learning procedures. Our simple classifier based on soft-thresholding also competes with the recent sparse coding classifiers, when the dictionary is learned appropriately. The adopted classification scheme further requires less computational time at the testing stage, compared to other classifiers. The proposed scheme shows the potential of the adequately trained soft-thresholding mapping for classification and paves the way towards the development of very efficient classification methods for vision problems. [ABSTRACT FROM AUTHOR]

It is an enormous challenge for imbalanced data learning in the field of machine learning. To construct balanced datasets, oversampling techniques have been studied extensively. However, many oversampling methods suffer from introducing noisy samples and blurring classification boundaries, leading to overfitting. To solve this problem, this paper proposes a new oversampling method, namely CS-SMOTE, for synthesizing minority class samples by three-point interpolation. CS-SMOTE is mainly based on the center offset factor and a synthesis strategy. First, the CS-SMOTE method removes noise samples, calculates the center offset factor, and selects sparsely distributed minority class samples by using the K-distance graph technique. Next, new samples are generated based on sparse minority samples, random minority samples, and sub-cluster centers located in the same sub-cluster samples. Finally, multiple comparative experiments on 18 well-known datasets demonstrate the effectiveness and general applicability of the proposed CS-SMOTE method for the imbalanced data classification. The experiments show that CS-SMOTE outperforms other competitors in terms of classification accuracy, while avoiding the issue of overfitting. [ABSTRACT FROM AUTHOR]

Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research. [ABSTRACT FROM AUTHOR]

Model-based diagnosis (MBD) has been widely acknowledged to be an effective fault diagnosis paradigm for combinational circuits. Most diagnosis algorithms (DAs) return a single diagnosis, a list of diagnoses or a score for every returned diagnosis, which estimates the likelihood of each diagnosis to be correct. Roni Stern et al. recently proposed the heath state as an output of DA to provide a manageable view of which components are likely to be faulty for human operators in the circuit diagnosis area. The health state can be used widely in areas such as troubleshooting problems, among others. This paper proposes a novel approach that improves the quality of health state with difference degree (IHSD) utilizing the MBD approach with multiple observations in the circuit diagnosis area, which returns a list of possible diagnoses that explain these observations simultaneously. In addition, we also propose a multi-observation “scoring” method based on the difference degree for every returned diagnosis, where the difference degree denotes the “distance” from the multi-observations diagnosis to the relevant diagnosis that satisfies only one observation. We present evidence that shows that, compared with the state-of-the-art algorithm on health state (Stern et al., Artif Intell 248:26-45 2018), our approach improves the quality of health state. [ABSTRACT FROM AUTHOR]

This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explicitly the area under the receiver operating characteristics (ROC) curve for binary classification problems instead of the classification accuracy, which is frequently not appropriate for classifier evaluation. This is particularly important in case of overlapping class distributions, when the user has to decide about the trade-off between high true-positive and good false-positive performance. The model keeps the idea of learning vector quantization based on prototypes by stochastic gradient descent learning. For this purpose, a GLVQ-based cost function is presented, which describes the area under the ROC-curve in terms of the sum of local discriminant functions. This cost function reflects the underlying rank statistics in ROC analysis being involved into the design of the prototype based discriminant function. The resulting learning scheme for the prototype vectors uses structured inputs, i.e. ordered pairs of data vectors of both classes. [ABSTRACT FROM AUTHOR]

In this paper, a more general version of the flow shop scheduling problem with the objective of minimizing the total flow time is investigated. In order to get closer to the actual conditions of the problem, some realistic assumptions including non-permutation scheduling, learning effect, multiple availability constraints, and release times are considered. It is assumed that the real processing time of each job on a machine depends on the position of that job in the sequence, and after processing a specified number of jobs at each machine, an unavailability period is occurring because of maintenance activities. Moreover, it is supposed that each job may not be ready for processing at time zero and may have a release time. According to these assumptions, a new mixed integer linear programming (MILP) model is proposed to formulate the problem. Due to the high complexity of the problem, a heuristic method and a simulated annealing algorithm are presented to find the nearly optimal solutions for medium- and large-sized problems. To obtain better and more robust solutions, the Taguchi method is used in order to calibrate the simulated annealing algorithm parameters. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution methods. [ABSTRACT FROM AUTHOR]