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This paper appends three proposed heuristic algorithms, viz. Initialization Algorithm, Fraction Repair Algorithm and Negative Repair Algorithm, to Particle Swarm Optimization (PSO) and extends its application to Pure Integer Linear Solid Transportation Problem (STP). The chief contribution of the paper is that the proposed Hybrid Particle Swarm Optimization (HPSO) algorithm discretizes the continuous search space of PSO so that it can operate in the discrete solution space of STP with any of the valid parameter settings. The Initialization Algorithm is developed to generate the sufficient number of random solutions for initiating HPSO with a diverse population of given size. Whereas Fraction Repair and Negative Repair Algorithms are developed to ensure that each successive population obtained with HPSO remains in the solution space of STP. The validity of HPSO is examined and illustrated by solving a numerical problem with certain parameter settings. Further, the HPSO demonstrates its ability to obtain an optimal or near optimal solution along with the alternate optimal solution, if it exists. It searches for the solutions even without adhering to the rigid conditions of traditional methods that every solution of STP must have a fixed number of non-zero decision variables. Moreover, the performance of HPSO is tested with three different parameter settings and compared amongst each other to analyze their significance in solving the problem. The performance of HPSO and the variants of parameters are statistically validated through an extensive experimental design. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed economic dispatch problem (EDP) in smart grids, where each generator only communicates with its neighbors and minimizes its own cost in the presence of network-wide equality constraints and local capacity limits. Distributed algorithms to solve this problem have become an attractive focus of engineering research due to their multiple advantages. Many existing methods are time-triggered, whereas few works have been dedicated to solving the problem using an event-triggered approach. This problem gets more challenging when further accelerated convergence is desired. To solve such a problem, we carefully design an event-triggered parameter distributed accelerated algorithm, named as ET-PDA. On the one hand, the choice of different parameters in ET-PDA will lead to different momentum (Nesterov or heavy-ball) methods, which facilitates the accelerated convergence of the algorithm. On the other hand, the event-triggered mechanism in ET-PDA makes the time gap between two continuous interaction moments of each generator larger than the iteration interval, contributing to improved communication efficiency. In particular, ET-PDA achieves linear convergence when the local cost function of each generator is smooth and strongly convex, moreover, it precludes Zeno-like behavior. The effectiveness of ET-PDA is also verified through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

• A many objective evolutionary algorithm based on decomposition and moth search is proposed. • Random and adaptive weights is used to break the limitation of uniform distribution weights. • Mutual evaluation value is used to evaluate the optimal individual in the neighborhood. • Improving scale factor α in MSA is to improve the performance of the proposed algorithm. Decomposition-based multi-objective evolutionary algorithm (MOEA/D) has good performance in solving multi-objective problems (MOPs) but poor performance in solving many-objective optimization problems (MaOPs). The weight vectors in MOEA/D are relatively fixed, which results in poor performance when dealing with complex MaOPs. In this paper, random and adaptive weights are introduced into MOEA/D to break the limitation of fixed weight vectors. And the moth search algorithm (MSA) is used as an operator to improve global search ability. The updating strategies in MSA are more consistent with the neighborhood learning strategy adopted in MOEA/D. In addition, to enable MSA to find the optimal solution in the neighborhood on the MaOPs to update other individuals. This paper introduces mutual evaluation value for evaluating the optimal individual in the neighborhood, and the proposed algorithm is abbreviated as MOEA/DMS. In comparative experiments on the MaF test suite, hypervolume (HV) and inverted generational distance (IGD) are used to measure MOEA/DMS and other many-objective evolutionary algorithms (MaOEAs). The results show that MOEA/DMS has an excellent performance in dealing with MaOPs. Besides, MOEA/DMS is compared with other state-of-the-art MaOEAs on two combinatorial MaOPs. The results show that MOEA/DMS also has significant advantages in dealing with combinatorial MaOPs. [ABSTRACT FROM AUTHOR]

This paper aims to refine and expand the theoretical and application framework of higher-dimensional digital chaotic system (HDDCS). Topological mixing for HDDCS is strictly proved theoretically at first. Topological mixing implies Devaney’s definition of chaos in a compact space, but not vice versa. Therefore, the proof of topological mixing promotes the theoretical research of HDDCS. Then, a general design method for constructing HDDCS via loop-state contraction algorithm is given. The construction of the iterative function uncontrolled by random sequences (hereafter called iterative function) is the starting point of this research. On this basis, this paper put forward a general design method to solve the construction problem of HDDCS, and several examples illustrate the effectiveness and feasibility of this method. The adjacency matrix corresponding to the designed HDDCS is used to construct the chaotic Echo State Network (ESN) for predicting Mackey-Glass time series. Compared with other ESNs, the chaotic ESN has better prediction performance and is able to accurately predict a much longer period of time. [ABSTRACT FROM AUTHOR]

In this paper, a chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm is proposed. The improved algorithm is used to solve the problems that the whale optimization algorithm's convergence accuracy and convergence speed are insufficient. Firstly, the proposed algorithm is initialized by a method based on chaotic maps and quadratic opposition-based learning strategy to obtain a population with better ergodicity. Secondly, by introducing an adaptive variable speed adjustment factor, each search link unites to form a negative feedback regulation network, thereby effectively balancing the algorithm's exploration ability and exploitation ability. Finally, 20 benchmark test functions and 3 complex constrained engineering optimization problems were used to conduct extensive tests on the improved algorithm. The results show that the improved algorithm has better performance than others in terms of convergence speed and convergence accuracy in a majority of cases, and can effectively jump out of the local optimum. [Display omitted] • Firstly, this paper proposed a quadratic Opposition-Base Learning strategy based on Bernoulli map, and successfully obtained a higher quality initial whale population. • Secondly, this paper proposed a new variable speed adjustment factor and a cooperative convergence factor, and combine them with individual fitness to form a negative feedback adjustment network. Experiments show that the algorithm has good self-adjusting ability. • Finally, the range of some of the original random numbers in whale optimization algorithm has been changed. This adjustment allows the algorithm to have a more variable search range. [ABSTRACT FROM AUTHOR]

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness. [ABSTRACT FROM AUTHOR]

Recovering a low-rank matrix and a sparse matrix from an observed matrix, known as sparse and low-rank decomposition (SLRD), is becoming a hot topic in recent years. The most popular model for SLRD is to use the ℓ 1 norm and nuclear norm for the sparse and low-rank approximation. Since this convex model has certain limitations, various nonconvex models have been explored and found to be very promising. In this paper, we introduce a generalized nonconvex nonsmooth model for SLRD which covers a wide range of nonconvex surrogate functions that are continuous, concave and monotonically increasing on [ 0 , ∞) to approximate both the ℓ 0 norm and the rank function, such as ℓ p norm (0 < p < 1), Logarithm, Geman, SCAD and MCP functions. The choice of the nonconvex surrogates for the sparse and low-rank components can be different. Due to the nonconvexity and extensive options of the surrogates, the optimization problem is untractable. Based on the majorization-minimization (MM) algorithm, we propose a unified framework named MM-ADMM algorithm to solve this problem, which can be applied to all eligible surrogates as long as their supergradients are available. The constrained majorizing problems established under the MM framework can be easily solved by the alternating direction method of multipliers (ADMM). The theoretical convergence properties are investigated and proved, including the convergence of the sequence of objective function values generated by the designed algorithm and a weak convergence result related to the inner ADMM-iterations. Experiments on the synthetic data and real-world applications demonstrate the effectiveness of our designed MM-ADMM algorithm. [ABSTRACT FROM AUTHOR]

Velocity planning serves as an important issue in motion planning for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained optimization problem by assuming the velocity of vehicle to be a specific cubic polynomial on every elementary part to avoid a sudden acceleration in path switching. Finally, we use a modified projection particle swarm optimization (PPSO) algorithm to obtain the time-optimal velocity profile. The proposed method can generate a smooth time-optimal velocity profile while considering all possible relevant constraints. Three examples are provided on different types of path to demonstrate that the final velocity profile is efficient to avoid the sudden acceleration change. Furthermore, the modified PPSO algorithm in this paper is used to solve the optimization problem with high dimensional variables when its upper bound is known, which can not be achieved by the general PPSO algorithm. [ABSTRACT FROM AUTHOR]

Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. However, label ambiguity in the case of label imbalance has not been well solved. In this paper, a novel label imbalance-aware algorithm named PL-LIMAP is proposed to effectively solve this problem. Firstly, a partial manifold constrained low-rank representation method is proposed to learn the instance affinity matrix, which can identify instances with the same label by fully mining the manifold structure of the data. Moreover, for making full use of the information hidden in candidate labels, the Jaccard distance is introduced to measure the similarity between candidate labels to obtain the label affinity matrix. The combination of these two affinity matrices can achieve the purpose of disambiguation through complementary information. Meanwhile, aiming to determine the imbalance of ambiguous labels, specialized measures are proposed to evaluate the imbalance level in PLL. According to the imbalance level of different labels, an alternative weighting scheme is proposed to mitigate the imbalance of ambiguous labels. As a consequence, the ground-truth label of an instance can be found out even though its label information is not available in advance. Experiments reveal the overwhelming performances of PL-LIMAP. [ABSTRACT FROM AUTHOR]

Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run. [ABSTRACT FROM AUTHOR]

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

In this paper, a novel complete model-free integral reinforcement learning (CMFIRL) algorithm based fault tolerant control scheme is proposed to solve the tracking problem of steer-by-wire (SBW) system. We begin with the recognition that the reference errors can eventually converge to zero based on the command generator model. Then an augmented tracking system is constructed with a corresponding performance index which is considered as a type of actuator failure. By using the reinforcement learning (RL) technique, three novel online update strategies are respectively developed to cope with the following three cases, i.e., model-based, partially model-free, and completely model-free. Especially, the RL algorithm for the complete model-free case eliminates the constraints of requiring the known system dynamics in fault-tolerant tracking controlling. The system stability and the convergence of the CMFIRL iteration algorithm are also rigorously proved. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, two improved projection algorithms are proposed for a servo turntable system based on the autoregressive exogenous (ARX) model. The improvement pertains to the complexity, convergence speed and estimation accuracy of the algorithm. First, the ARX model structure to be identified is obtained from the open-loop velocity model of the servo turntable. To solve the problems of imprecise step size calculation and slow convergence, the adaptive step size momentum projection (AMP) algorithm is proposed, in which segmented adaptive step sizes are adopted and momentum factors are introduced. On this basis, the latest estimation-based hierarchical adaptive step size momentum projection (LE-AMP) algorithm, which has faster convergence, is proposed as a combination of the hierarchical strategy and the latest estimation approach. Second, the complexity and convergence performance of the proposed algorithms are compared and analyzed. Finally, the effectiveness of the algorithms is verified using two examples. Theoretical analysis and simulation experiments show that the AMP algorithm has excellent parameter estimation accuracy, and its convergence speed is further improved through the addition of the momentum factor and the latest estimation, and these improved strategies do not cause any significant increase in complexity. [ABSTRACT FROM AUTHOR]

In this paper, we design a low-complexity direction-of-arrival (DOA) estimation algorithm based on the unitary approximate message passing (UAMP) and Bernoulli-Gaussian (BG) prior. We first show that the estimation of DOA can be transferred into a sparse signal recovery problem, where we turn to UAMP with damping technique to solve this problem. Furthermore, we assume the BG prior on the sparse vector to be estimated, resulting in the fast estimation of the positions of non-zero elements. Moreover, expectation maximum (EM) is leveraged to automatically learn the BG parameters. Compared to the state-of-the-art UAMP-based algorithm with sparse Bayesian learning (SBL), the proposed approach can achieve the same DOA mean square error (MSE) performance with a much faster convergence speed. [ABSTRACT FROM AUTHOR]

• A supervised multi-view model is proposed to realize a block-based feature selection. • The proposed model is composed of all sharing sub-models in each class. • The sparse regularizer can enhance the sparsity of blocks from features and views. • The proposed algorithm can realize the block separation and independent solution. • Numerical experiments show the effectiveness of our method on large-scale datasets. With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view's locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed. [ABSTRACT FROM AUTHOR]

• purely distributed ensemble clustering method. • privacy preserving algorithm for diffused data. • collaborative solution with validated conflict resolution. • consensus among agents in the network. In this paper, we consider the problem of distributed unsupervised learning where data to be clustered are partitioned over a set of agents having limited connectivity. In order to solve this problem, we consider a novel and extended ensemble clustering procedure in order to make it suitable to a fully distributed scenario. The proposed algorithm can deal with the case where each agent has a local and different dataset. Additionally, to reduce the total amount of exchanged information, only the local prototypes of clusters are forwarded among the neighbors. Cluster similarity indexes are adopted to solve conflicts among agents and to achieve a common structure at the end of the communication process. The experimental results prove the feasibility of this approach, which is able to reach an optimal performance when compared to a fully centralized implementation, that is where data is collected beforehand on a single clustering agent. [ABSTRACT FROM AUTHOR]

As a popular sampling tool used for Monte Carlo computing, importance sampling (IS) has been used in a wide variety of application areas recently. In large-scale survey data such as Chinese General Social Survey (CGSS), missing data, a high level of skewness and heteroscedastic variances commonly occur. Most of the existing literatures on survey data analysis focused on modeling the conditional mean without deep investigation about missing data. In this paper, we study an IS imputation algorithm and its modified algorithm with inverse probability weighting arrangement in quantile regression with missing covariates. We make full use of the observed data compared with the existing complete cases analysis and inverse probability weighting method, and also improve the computational efficiency of multiple imputation and EM-based algorithms. The quantile regression framework allows us to obtain an overall picture of covariates' effects, highlights the changing relationships according to the explored quantile of interest, and solves the problems of skewness and heterogeneity. Through simulation studies, we investigate the performances of both IS and ISW with other existing algorithms. Finally, we apply our algorithms to part of annual income data from CGSS in 2010. We build three kinds of quantile regression models based on all the subjects, urban areas subjects and rural areas subjects, respectively. [ABSTRACT FROM AUTHOR]

Kernel weighted fuzzy local information c-means clustering (KWFLICM) is of great significance in the field of fuzzy clustering. Nevertheless, it is sensitive to high noise or outliers. Aiming at the shortcoming of KWFLICM algorithm, this paper proposes a novel fuzzy local information c-means clustering algorithm utilizing total Bregman divergence driven by polynomial kernel function (TKWFLICM). Firstly, total Bregman divergence (TBD) is kernelized by global polynomial kernel function to solve the problem of kernel-based distance metric without rotation-invariance. Secondly, TBD with polynomial kernel metric is modified by local neighborhood information of current pixel to further enhance the ability to suppress the noise in image. In the end, the modified TBD with polynomial kernel is introduced into the objective function of KWFLICM, and then a novel robust fuzzy clustering with high robustness is obtained. Experimental results show that the proposed algorithm performs better than existing state-of-the-art KWFLICM algorithm, and the kernelized TBD can also be utilized in fuzzy clustering-related algorithms, which will enhance the anti-noise robustness of these algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Purpose of this paper are Real power loss reduction, voltage stability enhancement and minimization of Voltage deviation. Design/methodology/approach: In HLG approach as per Henry gas law sum of gas dissolved in the liquid is directly proportional to the partial pressure on above the liquid. Gas dissolving in the liquid which based on Henry gas law is main concept to formulate the proposed algorithm. Populations are divided into groups and all the groups possess the similar Henry constant value. Exploration and exploitation has been balanced effectively. Ranking and position of the worst agents is done in order to avoid the local optima. Then in this work Mobula alfredi optimization (MAO) algorithm is projected to solve optimal reactive power problem. Foraging actions of Mobula alfredi has been imitated to design the algorithm. String foraging, twister foraging and backward roll foraging are mathematically formulated to solve the problem. In the entire exploration space the Mobula alfredi has been forced to discover new regions by assigning capricious position. Through this approach, exploration competence of the algorithm has been improved. In all iterations, the position of the Mobula alfredi has been updated and replaced with the most excellent solution found so far. Exploration and exploitation capabilities have been maintained sequentially. Then in this work balanced condition algorithm (BCA) is projected to solve optimal reactive power problem. Proposed BCA approach based on the conception in physics- on the subject of the mass; incoming, exit and producing in the control volume. Preliminary population has been created based on the dimensions and number of particles and it initialized capriciously in the exploration space with minimum and maximum concentration. Production control parameter and Production probability utilized to control the exploration and exploitation. Findings: Proposed Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA are evaluated in IEEE 30 bus system with L-index (Voltage stability) and also tested in standard IEEE 14, 30, 57, 118, 300 bus test systems without L- index. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained. Originality/value: For the first time Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA is projected to solve the power loss reduction problem. [ABSTRACT FROM AUTHOR]

Abstract In this paper we study the reachable region queries for objects moving in obstacle space. Loosely speaking, given any one of moving objects and many other information, we are asked to return the whole region in which each location has the non-zero probability to be reachable for the moving object. To solve this problem, this paper presents an efficient algorithm that runs in O (n log n) time and consumes O (n log n) space, where n is the number of vertices of obstacles. Moreover, this paper establishes the lower bound for the proposed problem, showing that our solution essentially is optimal in terms of random access memory (RAM) model. Empirical study based on both real and synthetic datasets demonstrates the effectiveness and efficiency of our solution. [ABSTRACT FROM AUTHOR]

In this paper, optimization of a linear objective function subject to a generalized fuzzy relational inequalities is investigated in which an arbitrary continuous t-norm is considered as fuzzy composition, and fuzzy inequality replaces ordinary inequality in the constraints. Unlike most optimization algorithms, in fuzzy relational inequalities with fuzzy constraints (FRI-FC) we find a near-feasible solution having a better objective value than those resulting from the resolution of the similar problems with crisp (ordinary) inequality constraints. Such solutions are called super-optima in this paper. Subsequently, an algorithm is proposed to find a super-optimum with pre-specified desirable infeasibility. For this purpose, we firstly study some structural properties of the FRI-FC problem and present a new formulization that is independent of t-norms used in the constraints of the problem. This new formulation converts the primary problem into an equivalent problem with simple constraints without considering any penalty parameters. However, it is proved that the transformed equivalent problem enables our algorithm to distinguish unfavorable points and generate a sequence of solutions converging to a super-optimum under a certain sufficient condition. Finally, a modified PSO is presented in which the ability of PSO to solve unconstrained problems with continuous domain, the structure of the transformed problem and some fuzzy structural modifications are combined and form an efficient algorithm to solve the generalized FRI-FC problems. The modified PSO algorithm has been applied to the generalized FRI-FC problem defined with ten well-known continuous t-norms. Additionally, an idea of the FRI-FC problems as outer approximators for FRI problems with ordinary inequalities is also investigated. [ABSTRACT FROM AUTHOR]

The construction of truly competitive boundary-element (BE) codes, capable of analyzing real-life engineering problems, unavoidably requires the devising of coupling strategies to make the modeling of complex heterogeneous domains user-friendly. Thereby, high-performance algorithms for solving the highly sparse resulting system of equations are essential. Moreover, as discontinuous boundary elements are necessary to alleviate the modeling process of coupled domains, efficient (low-order) quadratures for integrating singular and nearly-singular fundamental kernels over the boundary elements must be implemented. This paper newly discusses the construction of the boundary-element subregion-by-subregion (BE SBS) technique based on a BEM formulation for torsion problems in general composite bars. One also presents details of the formulation of the Krylov solvers BiCG and BiCGSTAB-(l), embedded in the coupling algorithm. In addition, the BE SBS matrix structure itself is used to form an efficient sparse incomplete LU factorization (SILU) preconditioner to accelerate the iterative solution process. Torsion problems in bars with complex composite patterns (e.g. with many different materials) are analyzed to attest the efficiency and robustness of the whole boundary-element technique. [ABSTRACT FROM AUTHOR]

Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

• Modified compact utility list (MCUL) storage structure is built. • Backward checking is performed to reduce the search space and runtime. • Forward checking is combined with building candidates to reduce the runtime. • Similar transactions or closed transactions are merged to reduce storage space. • Values of LA-prune and C-prune are updated as soon as an MCUL is pruned. Mining closed high utility itemsets (CHUIs) involves finding a representative set of HUIs that is usually smaller than that of HUIs but can generate the full HUIs without loss of information. Researchers have therefore shown interest in this problem, and many methods have been proposed for mining CHUIs effectively, of which CHUI-Miner and EFIM-Closed are the two most efficient algorithms. However, these face performance issues when mining CHUIs from sparse datasets. In this paper, we propose a method for the effective mining of CHUIs in both dense and sparse datasets. We first modify the compact utility list structure in the HMiner algorithm to reduce the mining time, and then develop backward and forward checking methods using the most recently explored CHUIs and combine this with candidate building for the next levels. Finally, we apply pruning strategies to reduce the search space for the generation of CHUIs. Our experimental results show that the proposed algorithm, called HMiner-Closed, is more efficient than the state-of-the-art algorithms for both dense and sparse datasets. [ABSTRACT FROM AUTHOR]

In this paper, we study the regularization l p -norm minimization problem to recover the sparse signals. We first prove that every global optimal solution to the regularization l p -norm minimization problem also solves the l 0 -norm minimization problem if the certain conditions are satisfied, and then generate a truncated approximated difference algorithm to recover the sparse signals. At last, we provide some numerical simulations to test the performance of the truncated approximated difference algorithm, and the numerical results show that the proposed algorithm performs effectively in recovering the sparse signals compared with some state-of-art methods. [ABSTRACT FROM AUTHOR]

In underwater acoustics, compressive sensing was applied to the separation of multiple raypaths based on direction of arrival (DOA) estimation. While it is impossible to separate raypaths directly without eliminating the interference between raypaths. This paper proposes a subspace-based higher-order compressive sensing algorithm, which enables separating raypaths interrupted by colored noise. In the algorithm, a trispectrum matrix is first calculated from the signal received on the reception array. To improve the robustness against noise, the trispectrum matrix is decomposed into signal and noise subspaces using an eigenvalue decomposition. Then, a subspace-based higher-order convex optimization problem is designed based on the signal subspace. The separation of raypaths is cast as the optimization problem solved in a greedy strategy approximation. The results obtained in both simulations and experiments show that the present method not only achieves the more precise separation of raypaths, but it also provides better suppression of noise as compared to the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition to the traditional probability as well as to the fuzzy logic. It is centred at non-parametric density estimation derived from the data sample. In addition, the proposed framework is computationally cheap and provides fast and exact per-point processing of the data set/stream. The algorithm is demonstrated to be suitable for different classification tasks. Throughout the paper we give evidence of its applicability to a wide range of practical problems. Furthermore, the algorithm can be easily adapted to different classical data analytics problems, such as clustering, regression, prediction, and outlier detection. Finally, it is very important to remark that the proposed algorithm can work “from scratch” and evolve its structure during the learning process. [ABSTRACT FROM AUTHOR]

Highlights • A Quick Inclusion-Exclusion (QIE) technique is proposed. • The proposed QIE is for multi-state flow network reliability problems. • The proposed QIE is easy to code and only needs one comparision in each (intersection) term. • The proposed QIE is simpler than the traditional IE. • The proposed QIE is faster than the RSDP which is the most popular sum-of-disjoint product method. Abstract The reliability of modern information systems modeled as multi-state flow networks (MFNs) is a crucial concern the planning, design and control of these systems. The inclusion-exclusion technique (IET) is a popular tool for assessing MFN reliability because it is simple and easily understood. However, it is less efficient than other methods, like the sum-of-disjoint product method (SDP), for example. This paper proposes a new IET, called the Quick Inclusion-Exclusion technique (QIE) to increase the efficiency of the IET and reduce the amount of memory required in MFNs. The correctness and the time complexity of QIE is analyzed and proven. An MFN reliability example is implemented to illustrate the proposed QIE. In order to demonstrate its performance, the proposed QIE is compared with the most popular SDP, the recursive SDP (RSDP) in 20 benchmark networks taken from the literature. Numerical examples demonstrate that the proposed QIE outperforms RSDP in terms of both efficiency and memory use. This result differs significantly from those obtained by traditional methods. [ABSTRACT FROM AUTHOR]

Abstract Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs. [ABSTRACT FROM AUTHOR]

The use of Radar Cross Section (RCS) sequences for spatial target structure recognition is an important part of space situational awareness. RCS sequence is easily affected by the target physical characteristics and attitude characteristics, and the nonstationary characteristics of the sequence signal are obvious. In this paper, deep neural network (DNN) algorithm was used to solve the problem of spatial target structural feature recognition. For the problem of feature extraction without distinguishing degree, fractal features were used to extract the fractal features of RCS sequences, and the Fisher′s decision rate was used for selecting traditional features. What′s more, the DNN algorithm and data processing process were introduced. Finally, a set of simulation test data were used to verify the algorithm. The analysis results show that the DNN algorithm is robust and accurate in solving the problem of using RCS sequence to identify the target structure. [ABSTRACT FROM AUTHOR]

Purpose Ordinary differential equations (ODEs) are widely used in the engineering curriculum. They model a spectrum of interesting physical problems that arise in engineering disciplines. Studies of different types of ODEs are determined by engineering applications. Various techniques are used to solve practical differential equations problems. This paper aims to present a computational tool or a computer-assisted technique aimed at tackling ODEs. This method is usually not taught and/or not accessible to undergraduate students. The aim of this strategy is to help the readers to develop an effective and relatively novel problem-solving skill. Because of the drudgery of hand computations involved, the method requires the need to use computers packages. In this work, the successive differentiation method (SDM) for solving linear and nonlinear and homogeneous or non-homogeneous ODEs is presented. The algorithm uses the successive differentiation of any given ODE to determine the values of the function’s derivatives at a single point, mostly x = 0. The obtained values are used to construct the Taylor series of the solution of the examined ODE. The algorithm does not require any new assumption, hence handles the problem in a direct manner. The power of the method is emphasized by testing a variety of models with distinct orders, with constant and variable coefficients. Most of the symbolic and numerical computations can be carried out using computer algebra systems.Design/methodology/approach This study presents a computational tool or a computer-assisted technique aimed at tackling ODEs. This method is usually not taught and/or not accessible to undergraduate students. The aim of this strategy is to help the readers to develop an effective and relatively novel problem-solving skill. Because of the drudgery of hand computations involved, the method requires the need to use computers packages.Findings This method is applied to a variety of well-known equations, such as the Bernoulli equation, the Riccati equation, the Abel equation and the second-order Euler equation, some with constant and variable coefficients. SDM handles linear and nonlinear and homogeneous or nonhomogeneous ODEs in a direct manner without any need to restrictive conditions. The method works effectively to the Volterra integral equations, as will be discussed in a coming work.Originality/value The method can be extended to a wide range of engineering problems that are modeled by differential equations. The method is simple and novel and highly accurate. [ABSTRACT FROM AUTHOR]

This paper suggests a bidirectional relationship between state-space and problem- reduction representations. It resents a formalism based on multiple-input and multiple-output operators which provides a basis for viewing the two types of representations in this manner. A representation of the language recognition problem which is based on the Cocke parsing algorithm Is used as an illustration. A method for representing problems in first-order logic in such a way that the inference system employed by a resolution-based theorem prover determines whether the set of clauses is interpreted in the state-space mode or in the problem-reduction mode is presented. The analogous concepts in problem-reduction and theorem proving, and the terminology used to refer to them, are noted. The relationship between problem-reduction, input resolution, and linear resolution is is discussed. [ABSTRACT FROM AUTHOR]

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice’s (1976) selection framework. We describe each of the four component spaces – problem, algorithm, performance and characteristic – in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions. [ABSTRACT FROM AUTHOR]

This paper presents efficient chaotic invasive weed optimization (CIWO) techniques based on chaos for solving optimal power flow (OPF) problems with non-smooth generator fuel cost functions (non-smooth OPF) with the minimum pollution level (environmental OPF) in electric power systems. OPF problem is used for developing corrective strategies and to perform least cost dispatches. However, cost based OPF problem solutions usually result in unattractive system gaze emission issue (environmental OPF). In the present paper, the OPF problem is formulated by considering the emission issue. The total emission can be expressed as a non-linear function of power generation, as a multi-objective optimization problem, where optimal control settings for simultaneous minimization of fuel cost and gaze emission issue are obtained. The IEEE 30-bus test power system is presented to illustrate the application of the environmental OPF problem using CIWO techniques. Our experimental results suggest that CIWO techniques hold immense promise to appear as efficient and powerful algorithm for optimization in the power systems. [ABSTRACT FROM AUTHOR]

In the framework of open source CFD code OpenFOAM, a density-based solver for all speeds flow field is developed. In this solver the preconditioned all speeds AUSM+(P) scheme is adopted and the dual time scheme is implemented to complete the unsteady process. Parallel computation could be implemented to accelerate the solving process. Different interface reconstruction algorithms are implemented, and their accuracy with respect to convection is compared. Three benchmark tests of lid-driven cavity flow, flow crossing over a bump, and flow over a forward-facing step are presented to show the accuracy of the AUSM+(P) solver for low-speed incompressible flow, transonic flow, and supersonic/hypersonic flow. Firstly, for the lid driven cavity flow, the computational results obtained by different interface reconstruction algorithms are compared. It is indicated that the one dimensional reconstruction scheme adopted in this solver possesses high accuracy and the solver developed in this paper can effectively catch the features of low incompressible flow. Then via the test cases regarding the flow crossing over bump and over forward step, the ability to capture characteristics of the transonic and supersonic/hypersonic flows are confirmed. The forward-facing step proves to be the most challenging for the preconditioned solvers with and without the dual time scheme. Nonetheless, the solvers described in this paper reproduce the main features of this flow, including the evolution of the initial transient. [ABSTRACT FROM AUTHOR]

Recently, event-based social networks (EBSNs) have become popular, hence how to organize a social event has received significant attention. Most of prior studies about social events organization usually consider the willingness of attendees and their relationships. However, they ignore the roles of attendees. In fact, many social events have requirement of attendees roles in the real world. In this paper, we propose to study the problem of Multi-Role Social Event Organization (MRSEO). Our goal is to maximize the overall harmony of the social event while considering multiple factors, such as attendees’ roles, willingness and their relationships. To solve the problem, we propose two algorithms. Firstly, we propose a continuous relaxation technique based algorithm, called MRSEO-CRA. It converts the problem of MRSEO to an equivalent unconstrained continuous problem, and then employs RatioDCA algorithm to solve the converted one. Secondly, to better trade off between performance and running time, we further propose the other algorithm based on improved PageRank, called MRSEO-IPR. We conduct extensive experiments on real-world datasets to evaluate these two proposed algorithms and experimental results show that our algorithms outperform the state-of-the-art algorithm in terms of performance and running time. [ABSTRACT FROM AUTHOR]

Mining high utility patterns is an important data mining problem that is formulated as finding patterns whose utilities are no less than a threshold. As the mining results are very sensitive to such a threshold, it is difficult for users to specify an appropriate one. An alternative formulation of the problem is to find the top- n high utility patterns. However, the second formulation is more challenging because the corresponding threshold is unknown in advance and the solution search space becomes even larger. When there are very long patterns prior algorithms simply cannot work to mine top- n high utility patterns even for very small n . This paper proposes a novel algorithm for mining top- n high utility patterns that are long. The proposed algorithm adopts an opportunistic pattern growth approach and proposes five opportunistic strategies for scalably maintaining shortlisted patterns, for efficiently computing utilities, and for estimating tight upper bounds to prune search space. Extensive experiments show that the proposed algorithm is 1 to 3 orders of magnitude more efficient than the state-of-the-art top- n high utility pattern mining algorithms, and it is even up to 2 orders of magnitude faster than high utility pattern mining algorithms that are tuned with an optimal threshold. [ABSTRACT FROM AUTHOR]

Attribute reduction is a key aspect of Rough Set Theory. Finding the complete set of reducts is important for solving problems such as the assessment of attribute relevance, multi–objective cost–sensitive attribute reduction and dynamic reduct computation. The main limitation in the application of Rough Set methods is that finding all reducts of a decision system has exponential complexity regarding the number of attributes. Several algorithms have been reported to reduce the cost of reduct computation. Unfortunately, most of these algorithms relay on high cost operations for candidate evaluation. Therefore, in this paper, we propose a new algorithm for computing all reducts of a decision system, based on the pruning properties of gap elimination and attribute contribution , that uses simpler operations for candidate evaluation in order to reduce the runtime. Finally, the proposed algorithm is evaluated and compared with other state of the art algorithms, over synthetic and real decision systems. [ABSTRACT FROM AUTHOR]

In this paper we consider the impact of configuration of abstract argumentation reasoners both when using a single solver and choosing combinations of framework representation–solver options; and also when composing portfolios of algorithms. To exemplify the impact of the framework–solver configuration we consider one of the most configurable solvers, namely ArgSemSAT —runner-up of the last competition on computational models of argumentation (ICCMA-15)—for enumerating preferred extensions. We discuss how to configure the representation of the argumentation framework in the input file and show how this coupled framework–solver configuration can have a remarkable impact on performance. As to the impact of configuring differently structured portfolios of abstract argumentation solvers, we consider the solvers submitted to ICCMA-15, which provided the community with a heterogeneous panorama of approaches for handling abstract argumentation frameworks. A superficial reading of the results of ICCMA-15 is that reduction-based systems (either SAT-based or ASP-based) are always the most efficient. Our investigation, concerning the enumeration of stable and preferred extensions, shows that this is not true in full generality and suggests the areas where the relatively under-developed non-reduction-based systems should focus more to improve their performance. Moreover, it also highlights that the state-of-the-art solvers are very complementary and can be successfully combined in portfolios. [ABSTRACT FROM AUTHOR]

k -means is a preeminent partitional based clustering method that finds k clusters from the given dataset by computing distances from each point to k cluster centers iteratively. The filtering algorithm improves the performance of k -means by imposing an index structure on the dataset and reduces the number of cluster centers searched while finding the nearest center of a point. The performance of filtering algorithm is influenced by the degree of separation between initial cluster centers. In this paper, we propose an efficient initial seed selection method, RDBI, to improve the performance of k -means filtering method by locating the seed points at dense areas of the dataset and well separated. The dense areas are identified by representing the data points in a kd -tree. A comprehensive experimental analysis is performed to evaluate the performance efficiency of proposed method against state-of-the-art initialization methods and shown that the proposed method is efficient in terms of both running time and clustering accuracy. [ABSTRACT FROM AUTHOR]

Over the last two decades, many different differential evolution algorithms for solving optimization problems have been introduced. Although most of these algorithms used a single mutation strategy, several with multiple mutation strategies have recently been proposed. Multiple-operator-based algorithms have been proven to be more effective and efficient than single-operator-based algorithms for solving a wide range of benchmark and practical problems. In these algorithms, adaptive operator selection mechanisms are generally applied to place greater emphasis on the best-performing evolutionary operators based on their performance histories for generating new offspring. In this paper, we investigate using problem landscape information in an adaptive operator selection mechanism. For this purpose, a new algorithm, which considers both this problem landscape information and the performance histories of the operators, for dynamically selecting the most suitable differential evolution operator during the evolutionary process, is proposed. The contributions of each component of the selection mechanism are analyzed and the performance of the proposed algorithm is evaluated by solving 45 unconstrained optimization problems. The results demonstrate the effectiveness and superiority of the proposed algorithm to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

High-order finite difference and finite volume algorithms based on the coordinate transformation, which satisfy the property of freestream preservation are reevaluated in this paper. The intent here is to modify the conclusion drawn by Casper et al. [28], who claimed that the finite volume implementation was less sensitive to derivative discontinuities. Therefore, for problems with complex geometries, it might pay to use the finite volume algorithm. In the present work, all the cases from Casper et al. are simulated with two advanced algorithms combined with weighting techniques and the importance of the freestream preservation is demonstrated through the comparison. It is concluded that the finite difference algorithm with the freestream preservation satisfied performs as well as the finite volume algorithm and the time consumption of high-order finite difference algorithms is remarkably lower than that of finite volume algorithms in multiple dimensions. [ABSTRACT FROM AUTHOR]

Optimization design of energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV), which significantly affects the vehicle performance on fuel economy and pollutant emission, has always been a focal issue. Of various EMSs, rule-based strategies are dominant in practical applications due to their relatively low computational burden, but to obtain the optimum control parameters precisely and efficiently remains an unsolved problem. In this paper, a novel combinatorial algorithm utilizing the historical data from remote monitoring platform is proposed for the EMS optimization of PHEV. Firstly, the historical driving data are processed, and then a table which records different conditions at different time is built for reflecting the future PHEV operation schedule. Based on the historical data, a combinatorial algorithm which combines the advantages of genetic algorithm (GA) with enhanced ant colony algorithm (EACA) is proposed to optimize the control parameters. The principle of algorithm transformation from GA to EACA is when the objective function value is smaller than the default value after five generations of changing continuously in GA optimization process, and then the control parameter combinations can be regarded as the pheromone for EACA. Results show that the combinatorial algorithm successfully overcomes the low solution precision by GA and the slow resolving speed by EACA. The energy consumption of PHEV on a specific bus route can be reduced greatly by the proposed method, and it can provide a theoretical guidance for practical applications. [ABSTRACT FROM AUTHOR]

The purpose of this paper is deriving the minimal residual (MINIRES) algorithm for finding the symmetric least squares solution on a class of Sylvester matrix equations. We prove that if the system is inconsistent, the symmetric least squares solution can be obtained within finite iterative steps in the absence of round-off errors. Furthermore, we provide a method for choosing the initial matrix to obtain the minimum norm least squares symmetric solution of the problem. Finally, we give some numerical examples to illustrate the performance of MINIRES algorithm. [ABSTRACT FROM AUTHOR]

Conical functions appear in a large number of applications in physics and engineering. In this paper we describe an extension of our module Conical (Gil et al., 2012) for the computation of conical functions. Specifically, the module includes now a routine for computing the function R − 1 2 + i τ m ( x ) , a real-valued numerically satisfactory companion of the function P − 1 2 + i τ m ( x ) for x > 1 . In this way, a natural basis for solving Dirichlet problems bounded by conical domains is provided. The module also improves the performance of our previous algorithm for the conical function P − 1 2 + i τ m ( x ) and it includes now the computation of the first order derivative of the function. This is also considered for the function R − 1 2 + i τ m ( x ) in the extended algorithm. Program summary Program Title: Module Conical Program Files doi: http://dx.doi.org/10.17632/rpw5d8gdkg.1 Licensing provisions: CC by 4.0 Programming language: Fortran 90 External routines/libraries: The module Conical uses a Fortran 90 version of the routine dkia (developed by the authors) for computing the modified Bessel functions K i a ( x ) and its derivative. This routine is available at http://toms.calgo.org . Nature of problem: These functions are the natural function basis for solving, for example, the Laplace’s problem in spherical coordinates for two intersecting cones or for regions bounded by two intersecting spheres, or by one or two confocal hyperboloids of revolution when using toroidal coordinates. The conical function P − 1 2 + i τ m ( x ) is also used in the Mehler–Fock integral transform for problems in potential and heat theory, Solution method: The algorithm uses different methods of computation depending on the function under consideration ( P − 1 2 + i τ m ( x ) or R − 1 2 + i τ m ( x ) ) and the values of x , τ and m : numerical quadrature, asymptotic expansions in terms of elementary functions, asymptotic expansions in terms of Bessel functions, asymptotic expansions for tau large and backward/forward recursion of three-term recurrence relations. Restrictions: In order to avoid underflow/overflow problems in standard IEEE double precision arithmetic, the admissible parameter ranges for computing the conical function P − 1 2 + i τ m ( x ) in the routine conicp are: − 1 < x < 1 , 0 < τ < = 100 , 0 ≤ m ≤ 40 1 < x ≤ 100 , 0 < τ ≤ 100 , 0 ≤ m ≤ 100 . When using the routines conicr and conicpr , the admissible parameter ranges for computing the functions P − 1 2 + i τ m ( x ) and R − 1 2 + i τ m ( x ) are 1 < x ≤ 100 , 0 < τ ≤ 100 , 0 ≤ m ≤ 100 . [ABSTRACT FROM AUTHOR]

Combinatorial optimization involves more and more evaluation indicators, and some parameters cannot be accurately described. This paper considers a shortest path problem where arc costs include both uncertainty and randomness, and the decision-maker wishes to minimize both the expected cost and the variance of this cost. Firstly, a mean–variance model for the shortest path problem with uncertain arc cost and random arc cost is proposed, and the equivalent deterministic model of the model is deduced. Secondly, we develop a dynamic ripple spreading algorithm (DRSA) to solve the model, based on the ripple spreading patterns on the natural water surface. Then, the ripple spreading speed of the algorithm is simulated and predicted by hybrid prediction algorithm (HPA) on the basis of obtaining real urban traffic data, and it is verified by theoretical proof that DRSA can find the Pareto optimal path from the source node to the destination node within a single run. Finally, the proposed mean–variance shortest path problem model and DRSA are verified by numerical experiments. • A mean–variance model for the shortest path problem with uncertain arc cost and random arc cost is proposed. • A dynamic ripple spreading algorithm (DRSA) is proposed to solve the problem. • The ripple spreading speed of DRSA is simulated and predicted by hybrid prediction algorithm on the basis of obtaining real urban traffic data. • The complexity of dynamic algorithm is analyzed. • Experimental results demonstrate the superiority of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we address the optimal control problem for Boolean control networks (BCNs). We first consider the problem of finding the input sequences that minimize a given cost function over a finite time horizon. The problem solution is obtained by means of a recursive algorithm that represents the analogue for BCNs of the difference Riccati equation for linear systems. We prove that a significant number of optimal control problems for BCNs can be easily reframed into the present setup. In particular, the cost function can be adjusted so as to include penalties on the switchings, provided that we augment the size of the BCN state variable. In the second part of the paper, we address the infinite horizon optimal control problem and we provide necessary and sufficient conditions for the problem solvability. The solution is obtained as the limit of the solution over the finite horizon [0,T], and it is always achieved in a finite number of steps. Finally, the average cost problem over the infinite horizon, investigated in “Optimal control of logical control networks” (Y. Zhao , IEEE Trans. Autom. Control, vol 56, no. 8, pp. 1766–1776, Aug. 2011), is addressed by making use of the results obtained in the previous sections. [ABSTRACT FROM AUTHOR]