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In this paper, we consider the mixed and componentwise condition numbers for a linear function Lx of the solution to the total least squares (TLS) problem. We derive the explicit expressions of the mixed and componentwise condition numbers through the dual techniques under both unstructured and structured componentwise perturbations. The sharp upper bounds for condition numbers are obtained. An efficient condition estimation algorithm is proposed, which can be integrated into the iterative method for solving large scale TLS problems. Moreover, the new derived condition number expressions can recover the previous results on the condition analysis for the TLS problem when L = I n . Numerical experiments show the effectiveness of the introduced condition numbers and condition estimation algorithm. [ABSTRACT FROM AUTHOR]

Data warehouses exploit On-Line Analytical Processing (OLAP) to make rapid answers for analytical queries. Huge amount of aggregated data within a data warehouse on the one hand, and complex analytical queries raised in a data warehouse on the other hand, increase response time to queries extremely. To solve this problem, a number of views are derived and extracted from original base tables and queries have been answered using them. Since materialization of all possible views is not effective because of limitation of storage and maintenance overhead, selecting an optimal set of views for materialization is crucial to maximize data warehouse performance. In this paper, a game theory based framework for the materialized view selection is proposed. In the proposed framework, query processing and view maintenance costs play a game against each other as two players and continue the game until reach the equilibrium. According to the framework, a new static method, called Game Theory based Materialized View selection (GTMV), has been proposed. Verification of proposed approach has been evaluated using several synthetic and real world datasets. Experimental results show that the GTMV method has better performance comparing previous algorithms and substantially outperform former methods. [ABSTRACT FROM AUTHOR]

Many problems in applied geophysics can be formulated as a linear inverse problem. The associated problems, however, are large-scale and ill-conditioned. Therefore, regularization techniques are needed to be employed for solving them and generating a stable and acceptable solution. We consider numerical methods for solving such problems in this paper. In order to tackle the ill-conditioning of the problem we use blockiness as a prior information of the subsurface parameters and formulate the problem as a constrained total variation (TV) regularization. The Bregmanized operator splitting (BOS) algorithm as a combination of the Bregman iteration and the proximal forward backward operator splitting method is developed to solve the arranged problem. Two main advantages of this new algorithm are that no matrix inversion is required and that a discrepancy stopping criterion is used to stop the iterations, which allow efficient solution of large-scale problems. The high performance of the proposed TV regularization method is demonstrated using two different experiments: 1) velocity inversion from (synthetic) seismic data which is based on Born approximation, 2) computing interval velocities from RMS velocities via Dix formula. Numerical examples are presented to verify the feasibility of the proposed method for high-resolution velocity inversion. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms are widely used in mathematical and engineering optimization. As one of the latest swarm intelligence-based methods, fruit fly optimization algorithm (FOA) was proposed inspired by the foraging behavior of fruit fly. In order to overcome the shortcomings of original FOA, a new improved fruit fly optimization algorithm called IAFOA is presented in this paper. Compared with original FOA, IAFOA includes four extra mechanisms: 1) adaptive selection mechanism for the search direction, 2) adaptive adjustment mechanism for the iteration step value, 3) adaptive crossover and mutation mechanism, and 4) multi-sub-swarm mechanism. The adaptive selection mechanism for the search direction allows the individuals to search for global optimum based on the experience of the previous iteration generations. According to the adaptive adjustment mechanism, the iteration step value can change automatically based on the iteration number and the best smell concentrations of different generations. Besides, the adaptive crossover and mutation mechanism introduces crossover and mutation operations into IAFOA, and advises that the individuals with different fitness values should be operated with different crossover and mutation probabilities. The multi-sub-swarm mechanism can spread optimization information among the individuals of the two sub-swarms, and quicken the convergence speed. In order to take an insight into the proposed IAFOA, computational complexity analysis and convergence analysis are given. Experiment results based on a group of 29 benchmark functions show that IAFOA has the best performance among several intelligent algorithms, which include five variants of FOA and five advanced intelligent optimization algorithms. Then, IAFOA is used to solve three engineering optimization problems for the purpose of verifying its practicability, and experiment results show that IAFOA can generate the best solutions compared with other ten algorithms. [ABSTRACT FROM AUTHOR]

This paper applies a new hybrid method by a combination of three population base algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). The proposed method has been inspired from natural selection process and it completes this process in GA by using the PSO and SOS. It tends to minimize the execution time and in addition to reduce the complexity. Symbiotic organisms search is a robust and powerful metaheuristic algorithm which has attracted increasing attention in recent decades. There are three alternative phases in the proposed algorithm: GA, which develops and selects best population for the next phases, PSO, which gets experiences for each appropriate solution and updates them as well and SOS, which benefits from previous phases and performs symbiotic interaction update phases in the real-world population. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. It has further been evaluated in, the experiment on the clustering of benchmark datasets. The obtained results from basic and non-parametric statistical tests confirmed that this hybrid method dominates in terms of convergence, execution time, success rate. It optimizes the high dimensional and complex functions Rosenbrock and Griewank up to 10 − 330 accuracy in less than 3 s, out-performing other known algorithms. It had also applied clustering datasets with minimum intra-cluster distance and error rate. [ABSTRACT FROM AUTHOR]

This paper presents a novel time integration algorithm for solving linear structural dynamic problems in the framework of the high-order collocation method. When two Gauss points in the integration interval are selected as collocation points, both an A-stable algorithm with third order accuracy and a non-dissipative algorithm with fourth order accuracy can be derived from a second order collocation polynomial. The only difference is that the former obtains a numerical solution at the middle point of the time interval, while the latter has a solution at the end of the interval. A new composite method is established through applying these two algorithms alternately, which combines the advantages of the numerical dissipation property of the third order algorithm and the high-order accuracy of the fourth order algorithm. The usage frequency of the two algorithms during the whole step-by-step integration procedure is an important parameter affecting the numerical dissipation, which is investigated in this study. As the algebraic equations systems solved by the two algorithms are exactly same, no extra computation effort is introduced. [ABSTRACT FROM AUTHOR]

We report on the First International Competition on Computational Models of Argumentation (ICCMA'15) which took place in the first half of 2015 and focused on reasoning tasks in abstract argumentation frameworks. Performance of submitted solvers was evaluated on four computational problems wrt. four different semantics relating to the verification of the acceptance status of arguments, and computing jointly acceptable sets of arguments. In this paper, we describe the technical setup of the competition, and give an overview on the submitted solvers. Moreover, we report on the results and discuss our findings. [ABSTRACT FROM AUTHOR]

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. [ABSTRACT FROM AUTHOR]

This paper explores the improvement of the network robustness against cascading failures by edge addition. Several edge-adding strategies are compared, including the random edge-adding strategy, the high betweenness centrality edge-adding strategy, the high Pagerank centrality strategy and the min–max edge-adding strategy. In the min–max edge-adding strategy, the problem for robustness improvement of complex networks is formulated as a nonlinear integer programming problem, and an algorithm is proposed to solve this problem. From the numerical experiments, we find that the min–max edge-adding strategy is more effective in improving the network robustness against cascading failures than the other strategies. Finally, this work can provide guidance for the design of complex networks to prevent potential cascading failures. [ABSTRACT FROM AUTHOR]

Abstract: The crowd distribution information is the crucial information for abnormal behaviors detection in the crowd scenes. In this paper, we firstly refer to the definition of the entropy and propose an algorithm effectively and accurately representing the crowd distribution information in the crowd scenes. The proposed algorithm not only avoids unstable foreground extraction, but also owns low computational complexity. To detect the abnormal crowd behaviors, we use the Gaussian Mixture Model (GMM) over the normal crowd behaviors to predict the abnormal crowd behaviors since GMM usually can deal well with the unbalanced problem. In this paper we simultaneously use the crowd distribution information and the crowd speed information to estimate the parameters of GMM over the normal crowd behaviors and predict abnormal crowd behaviors. Experiment conducted on publicly available dataset consisting of gathering and dispersion events validates that the proposed approach can preeminently reflect the crowd distribution information. In addition, experiments conducted on publicly UMN dataset demonstrate that the proposed abnormal crowd behavior detection method has an excellent performance and outperforms the state-of-the-art methods. [Copyright &y& Elsevier]

Abstract: This paper investigates the solution algorithms for the multi-criteria multi-modal shortest path problem (M-SPP), which belongs to the set of problems known as NP-hard, in urban transit network (UTN). The related M-SPP is one of the important and practical problems in several fields such as urban transportation system and freight transportation. The UTN is composed of multiple modes (e.g., automobile, bus, subway, light rail, pedestrian and so on). To get their destination, the passengers can alternate between different modes. As a special demand, the time-window is usually associated with the M-SPP. Because of the service time-limit of modes, the available modes at a stop are varied with the time. So the optimal M-SPP with arriving time-window cannot be simply obtained by finding the optimal M-SPP firstly and then reversely deducing the leaving time-window of the origin according to the arriving time-window of destination. In this paper, the M-SPP with arriving time-window is firstly proposed. To solve the multi-criteria M-SPPs (MM-SPP) with transfer delaying, an improved exact label correcting algorithm (LCA) is designed and, to solve the proposed MM-SPPs with both of transfer delaying and arriving time-window, an exact reverse LCA is designed. Finally, some computing examples are given to test the effectiveness of the designed algorithms. [Copyright &y& Elsevier]

Abstract: This paper considers the problem of assigning independent jobs to identical parallel machines with nonsimultaneous starting times. The algorithm MULTIFIT has been applied for the problem but its tight worst-case performance ratio has been unknown. In this paper we prove that the exact performance ratio of MULTIFIT is . [Copyright &y& Elsevier]

Abstract: In this paper 1 [1] The extended abstract of this paper appeared on PPAM 2009, LNCS 6067 (2010) 11–20. we study the problem of interval incidence coloring of bipartite graphs. We show the upper bound for interval incidence coloring number ( ) for bipartite graphs , and we prove that holds for regular bipartite graphs. We solve this problem for subcubic bipartite graphs, i.e. we fully characterize the subcubic graphs that admit 4, 5 or 6 coloring, and we construct a linear time exact algorithm for subcubic bipartite graphs. We also study the problem for bipartite graphs with and we show that 5-coloring is easy and 6-coloring is hard ( -complete). Moreover, we construct an time optimal algorithm for trees. [Copyright &y& Elsevier]

Abstract: In this paper, an effective method based upon the piecewise homotopy perturbation method (PHPM) is proposed for finding the numerical solutions of Fredholm integro-differential equations with weakly singular kernels. For these equations, the traditional homotopy perturbation method is divergent. So we present a modified homotopy perturbation method for solving this kind of equation. The advantages of the approach of this paper lie in the feature that this technique not only achieves an approximate solution with high accuracy, but also improves the calculation speed. Two examples are given to show the effectiveness and convenience of this method. [Copyright &y& Elsevier]

Abstract: This paper investigates single-machine scheduling problems with piece-rate machine maintenance and interval constrained actual processing time. The actual processing time of a job is a general function of the normal job processing time and the position in job sequence, and it is required to restrict in given interval otherwise earliness or tardiness penalty should be paid. The maintenance duration studied in the paper is a time-dependent linear function. The objective is to find jointly the optimal frequency to perform maintenances and the optimal job sequence to minimize the total cost, which is a linear function of the makespan, total earliness and total tardiness. There is shown that the problem can be optimally solved in O(n 4) time. There is also shown that two special cases of the problem can be optimally solved by lower order algorithms. [Copyright &y& Elsevier]

Highlights: [•] Aiming at the problem that how to classify T-splines this paper gives a mathematical analysis, and a sufficient condition of standard T-splines is also given. [•] Then this paper presents an algorithm of determining the T-spline classification, and we can get the inherently mathematical properties of T-splines by the algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Using the semi-tensor product method, this paper investigates the algebraic formulation and strategy optimization for a class of evolutionary networked games with “myopic best response adjustment” rule, and presents a number of new results. First, the dynamics of the evolutionary networked game is converted to an algebraic form via the semi-tensor product, and an algorithm is established to construct the algebraic formulation for the game. Second, based on the algebraic form, the dynamical behavior of evolutionary networked games is discussed, and some interesting results are presented. Finally, the strategy optimization problem is considered by adding a pseudo-player to the game, and a free-type control sequence is designed to maximize the average payoff of the pseudo-player. The study of an illustrative example shows that the new results obtained in this paper work very well. [Copyright &y& Elsevier]

Abstract: The Single-Allocation Ordered Median Hub Location problem is a recent hub model introduced by Puerto et al. (2011) [32] that provides a unifying analysis of the class of hub location models. Indeed, considering ordered objective functions in hub location models is a powerful tool in modeling classic and alternative location paradigms, that can be applied with success to a large variety of problems providing new distribution patterns induced by the different users’ roles within the supply chain network. In this paper, we present a new formulation for the Single-Allocation Ordered Median Hub Location problem and a branch-and-bound-and-cut (B&B&Cut) based algorithm to solve optimally this model. A simple illustrative example is discussed to demonstrate the technique, and then a battery of test problems with data taken from the AP library are solved. The paper concludes that the proposed B&B&Cut approach performs well for small to medium sized problems. [Copyright &y& Elsevier]

Abstract: This paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE). [Copyright &y& Elsevier]

Abstract: This paper proposes a four dimensional orthogonal packing and time scheduling problem. The problem differs from the classical packing problems in that the position and orientation of each item in the container can be changed over time. In this way, the four dimensional space–time problem better uses the container time. Also, we consider a general case that all parameters are real numbers, which makes the problems more difficult to solve. This paper proposes an algorithm and proves that the algorithm could solve the problem optimally by a finite number of operations. We say this problem is weak computational, meaning that if there exists a universal machine that could represent real numbers and could do unit arithmetic or logical operation on real numbers in finite time, then the algorithm could find optimal solutions in finite time. This paper also presents a proof of the weak computability over a general case of the three dimensional orthogonal packing problem where all parameters are positive real numbers. [Copyright &y& Elsevier]

• A new BP-ANN calculation method for reactor fuel reloading optimization was developed. • The BP-ANNs developed in this paper have higher precision than those in similar paper. • Five different meta-heuristic algorithms were compared when combining with the BP-ANN calculation method for fuel reloading optimization. • The developed BP-ANN calculation method for reactor fuel reloading optimization obviously fasten the calculation time. In order to obtain optimal core configurations, the problems of fuel reloading optimization must be solved in nuclear engineering field. This problem is a combinational optimization problem with an enormous solution space. In the traditional calculation method, the meta-heuristic algorithms (MHAs) are used to search and update optimization solutions continuously, while the core physics calculation programs are used to calculate the optimization parameters. In this research, we developed a new BP-ANN calculation method to solve the problems of fuel reloading optimization for thorium-based block-type high temperature gas-cooled reactors (HTGRs). In this method, the function of core physics calculation programs are replaced by BP-ANNs in order to reduce calculation time. Besides, five different MHAs, which are genetic algorithm (GA), particle swarm optimization (PSO), Teaching-learning based optimization algorithm (TLBO), harmony search (HS) and Rank-based ant system (RAS), are used under this new method to solve the problems of fuel reloading optimization, respectively. The results shows that PSO is the best MHA with a real feasible solution ratio of 0.30 and the best fitness function value of 0.455904. Compared to the traditional calculation method, the calculation time of BP-ANN calculation method is reduced to 0.7% of the former. [ABSTRACT FROM AUTHOR]

This paper presents a blind image deblurring algorithm by utilizing Gaussian curvature (GC) of the image surface. GC is an intrinsic geometric feature and related to the developability of the surface. In recent years, numerous variational models based on GC for image denoising and image reconstruction have been proposed. In this paper, we show that GC regularization is also effective for blind image deblurring when combines it with L 0 -norm of image gradients. By minimizing the combined regularization, our algorithm gradually preserves sharp edges and removes detrimental structures and noises in intermediate latent images. The sharp latent images can then accurately guide the estimation of the blur kernel. However, a complicated optimization problem will occur once the proposed regularization has been involved. As we know, traditional diffusion methods for minimizing the GC regularization not only converges slowly but also requires the differentiability of signals. Moreover, the L 0 -norm of image gradients is non-convex. Consequently, Gaussian curvature filter (GCF) and the half-quadratic splitting strategy are adopted to solve the optimization problem. Extensive experimental results show that the proposed deblurring method achieves state-of-the-art results on benchmark datasets and performs favorably on real-world blurry images. • A novel regularization based on Gaussian curvature (GC) of the image surface for blind image deblurring is proposed. • Minimizing the proposed regularization is able to preserve sharp edges and remove detrimental structures for blur kernel estimation. • An effective optimization scheme based on Gaussian curvature filter (GCF) and half-quadratic splitting method is provided. • Effectiveness of the proposed method is evaluated on synthetic datasets and real-world blurry images. [ABSTRACT FROM AUTHOR]

Resource transfers and uncertain activity durations are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized multi-project scheduling problem with both resource transfers and uncertain activity durations, we name it the stochastic decentralized multi-project scheduling problem with resource transfers (SDRCMPSPTT). To tackle the curse-of-dimensionality of an exact solution approach, we develop a new and effective rollout policy-based approximate dynamic programming (ADP) algorithm for SDRCMPSPTT. Based on the benchmark instances, we build a new data set and examine the performance of 12 priority rule heuristics, then select the two best ones as the base policy for our rollout algorithm. The proposed approach is verified under a stochastic environment by assessing different base heuristic policies, computational results show that the rollout algorithm can further improve the solution quality of the base heuristics. Compared with the state-of-the-art algorithm for solving the decentralized multi-project scheduling problem under a deterministic environment, our rollout policy-based ADP algorithm can also obtain competitive solutions. • An MDP model is established to formulate the problem studied in this paper. • A new rollout-based ADP algorithm is developed to obtain closed-loop solutions. • Rollout algorithm performs better than the base heuristics significantly. • The rollout algorithm is further verified by solving deterministic problems. [ABSTRACT FROM AUTHOR]

• The data-driven inverse optimization corrects the DR execution online. • The proposed strategy is realized in a distributed manner and free of initialization. • This paper provides rigorous stability proof for the proposed optimization. • D-PPDS algorithm solves the DR optimization with global inequality constrain. This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader's strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. [ABSTRACT FROM AUTHOR]

This paper aims to develop a moving-mesh type Finite Element Method for the computation of the transient unconfined seepage flow through the porous medium. The proposed method is based on the time discontinuous Galerkin Space-Time Finite Element Method (ST/FEM). It solves the seepage problem in the saturated region. The primary unknown in ST/FEM is piezometric pressure. Fluid velocities are derived from the pressure using Darcy's law. Further, an iterative algorithm has been proposed in this paper to implement the proposed method. In each iteration step, the computation domain is updated according to the flow velocity on the phreatic boundary. Subsequently, internal nodes are moved using the mesh moving technique to accommodate the newly updated computation domain. The mesh moving technique, which is discussed in this paper, is based on an elasticity problem. ST/FEM is employed to analyze several unconfined seepage flow problems, and results of steady state solutions are compared with those available in the literature to demonstrate the efficacy of the proposed scheme. • Moving mesh type finite element method is developed for transient and unconfined seepage flow analysis. • The proposed scheme requires 2 to 3 iterations in a time step to achieve convergence. • Elasticity equation based automatic mesh moving technique is employed to move the internal nodes. • Initial computation domain significantly affects the deformation characteristics of mesh. • Proposed iterative scheme can be employed with any type of mesh moving technique. [ABSTRACT FROM AUTHOR]

• This paper proposes a decentralized ADAM-type algorithm for solving minmax optimization problem under the condition that the objective function satisfies Minty VI which is a generalization to convex-concave case. The proposed method is unlike the recent non-adaptive gradient-based decentralized min-maxstudies that fail drastically in practice and requires large efforts for tuning. In this paper, we first obtain non-asymptotic rates of convergence of our method for finding (stochastic) first-order Nash equilibrium point and then evaluate its performance by using it for training GANs in numerous dataset. In the revised version of the paper, we have pointed out relaxation of the Minty condition in the discussion of the paper. Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs). However, most of the recent efforts for solving them are limited to special regimes such as convex-concave games. Further, it is customarily assumed that the underlying optimization problem is solved either by a single machine or in the case of multiple machines connected in centralized fashion, wherein each one communicates with a central node. The latter approach becomes challenging, when the underlying communications network has low bandwidth. In addition, privacy considerations may dictate that certain nodes can communicate with a subset of other nodes. Hence, it is of interest to develop methods that solve min-max games in a decentralized manner. To that end, we develop a decentralized adaptive momentum (ADAM)-type algorithm for solving min-max optimization problem under the condition that the objective function satisfies a Minty Variational Inequality condition, which is a generalization to convex-concave case. The proposed method overcomes shortcomings of recent non-adaptive gradient-based decentralized algorithms for min-max optimization problems, that do not perform well in practice and require careful tuning. In this paper, we obtain non-asymptotic rates of convergence of the proposed algorithm (coined Dadam 3) for finding a (stochastic) first-order Nash equilibrium point and subsequently evaluate its performance on training GANs. The extensive empirical evaluation shows that Dadam 3 outperforms recently developed methods, including decentralized optimistic stochastic gradient for solving such min-max problems. [ABSTRACT FROM AUTHOR]

The recently introduced Marine Predators Algorithm (MPA) exhibits competitive performance in solving optimization problems. However, it often prematurely converges due to an imbalance between its exploration and exploitation capabilities. Therefore, in this paper, an improved MPA variant using a proposed Local Escaping Operator (LEO) is introduced. In the MPA, solution candidates are replaced by better candidates from the previous iteration, mimicking a memory of already visited prey-abundant areas. This indicates a weak inter-dependence between the candidates in the population and possible acceptance of new solution candidates created outside the algorithm without damaging the optimization process. Consequently, in the proposed approach, the worst candidates are replaced with solutions created by the LEO. The LEO is based on representative solutions, taking into account the relationship between predators and the characteristics of their population. The approach is experimentally compared with the state-of-the-art meta-heuristics on 82 test functions, including IEEE Congress on Evolutionary Computation test suites (CEC'14 and CEC'17) and three engineering problems. The results show the superiority of the LEO-MPA over the MPA and recent algorithms. Furthermore, in this paper, the suitability of the hybridization of meta-heuristics with the LEO is discussed and successful attempts with the best algorithms are shown. • A local escaping operator based on representative solutions is proposed. • Hybridization of the MPA algorithm with the LEO is introduced. • Introduced LEO-MPA is compared with 20 state-of-the-art meta-heuristics. • Suitability of the LEO to improve other meta-heuristics is investigated. [ABSTRACT FROM AUTHOR]

• Automatic Artificial Intelligence Algorithm Selection. • Automatic Hyperparameter Optimization. • Particle Swarm Optimization which can handle continuous and discrete values. • Particle Swarm Optimization with Simulated Annealing acceptance criteria. • Global Optimal Solution. Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way. [ABSTRACT FROM AUTHOR]

• It uses a simple mechanism and an unpredictable sequence to encrypt the image. • Four keys make it sensitive to changes in the original image. • The natural gene data bank is used to store the one-time pad. • It encrypts quickly enough to make it suitable for real-time applications. Image encryption is an important necessity when sharing data over public networks, and many image encryption algorithms have been proposed for the task. However, most of these algorithms are complex and slow. We propose a real-time one-time pad DNA-chaos image encryption algorithm using multiple keys. The proposed encryption algorithm uses a greatly simplified mechanism and a large, unpredictable sequence to encrypt the image. Our algorithm has three main advantages. First, its reliance on the original image for four of the keys makes it sensitive to changes in the original image. Small changes in the image produce large changes in the keys, making the algorithm resistant to differential attacks. Second, we integrate the natural DNA sequence into the process of generating the key, and the algorithm uses the massive DNA sequences stored in the DNA gene bank to solve the problem that it is difficult to store one-time pad. The keys have the property of one-time pad, and the key space is considerablely large. Finally, our algorithm encrypts quickly enough to make it suitable for real-time applications. On the premise of ensuring that the results of this paper are not worse than those of the comparison literature in other performance indicators, in terms of encryption time, even if we use a computing environment that is worse than all the comparison literature computer configurations, the algorithm of this paper can still save at least 14%–86%. As the picture size becomes larger, the advantages of the algorithm in this paper are more obvious. [ABSTRACT FROM AUTHOR]

• This paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level limited discussion in the open literature. We formulate an l_p-norm metric for pulse compression model which suits for either the ISL or PSL minimization only with different initializations of p. The waveform and filter can be updated simultaneously (not in alternating manner). A new algorithm with guaranteed convergence is proposed to solve the introduced problem. A number of new Lemmas are proved to find proper majorizers. Low sidelobe level is required for pulse compression (PC) radar systems since it can reduce false alarm triggered by the high sidelobe and improve the ability to detect weak targets nearby strong scattering points. In most of the existing literature, sidelobe suppression is achieved by either transmit waveform design at the transmitter or mismatched filter optimization at the receiver. Joint design of waveform and filter is an approach with limited discussion. Thus, this paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level. We first formulate an L p -norm metric for PC model which suits for either the integrated sidelobe level or peak sidelobe level (PSL) minimization (only with different initializations of p). Then we present a new iterative method to deal with the problem by using Dinkelbach's scheme and majorization minimization method. The proposed method is shown to be convergent and the computational complexity is also analyzed. Numerical examples demonstrate that waveforms and filters designed by using the proposed method produce lower PSL than the existing techniques. [ABSTRACT FROM AUTHOR]

Abstract: This paper offers a survey of recent work on particle swarm classification (PSC), a promising offshoot of particle swarm optimization (PSO), with the goal of positioning it in the overall classification domain. The richness of the related literature shows that this new classification approach may be an efficient alternative, in addition to existing paradigms. After describing the various PSC approaches found in the literature, the paper identifies and discusses two data-related problems that may affect PSC efficiency: high-dimensional datasets and mixed-attribute data. The solutions that have been proposed in the literature for each of these issues are described including recent improvements by a novel PSC algorithm developed by the authors. Subsequently, a positioning PSC for these problems with respect to other classification approaches is made. This is accomplished by using one proprietary and five well known benchmark datasets to determine the performances of PSC algorithm and comparing the obtained results with those reported for various other classification approaches. It is concluded that PSC can be efficiently applied to classification problems with large numbers of instances, both in continuous and mixed-attribute problem description spaces. Moreover, the obtained results show that PSC may not only be applied to more demanding problem domains, but it can also be a competitive alternative to well established classification techniques. [Copyright &y& Elsevier]

Abstract: In order to solve the problem that the general image compression algorithm and the running speed based on single DSP or PC cannot satisfied the demand for high-resolution mapping real-time image compression, this paper proposed a low-complexity rata pre-allocated algorithm and an implement mode. Firstly, the multi-level integer wavelet applied to image. Secondly, depend on the principle that various sub-band important to image reconstruction, and the optimal rate-distortion theory, pre-allocated the actual contribution rate in the coding for the each sub-band. Lastly, when each sub-band has been coded, the coding have been truncated in real-time according to the pre-allocated rate, so the bit rate size of the compressed image has been controlled precisely. Test results show that this paper proposed algorithm which has more features than the other classical standard compression algorithm. The features include less calculation, less usage of storage space, and simple structure of the bit rate stream. The algorithm with low complexity but the same compression performance as the JPEG2000 standard is significantly better than SPIHT and other algorithms. Finally, the algorithm proposed in this paper has been realizes in real-time by many DSP and FPGA. [Copyright &y& Elsevier]

Abstract: The environmental/economic dispatch (EED) is an important daily optimization task in the operation of many power systems. It involves the simultaneous optimization of fuel cost and emission objectives which are conflicting ones. The EED problem can be formulated as a large-scale highly constrained nonlinear multiobjective optimization problem. In recent years, many metaheuristic optimization approaches have been reported in the literature to solve the multiobjective EED. In terms of metaheuristics, recently, scatter search approaches are receiving increasing attention, because of their potential to effectively explore a wide range of complex optimization problems. This paper proposes an improved scatter search (ISS) to deal with multiobjective EED problems based on concepts of Pareto dominance and crowding distance and a new scheme for the combination method. In this paper, we have considered the standard IEEE (Institute of Electrical and Electronics Engineers) 30-bus system with 6-generators and the results obtained by proposed ISS algorithm are compared with the other recently reported results in the literature. Simulation results demonstrate that the proposed ISS algorithm is a capable candidate in solving the multiobjective EED problems. [Copyright &y& Elsevier]

Abstract: This paper considers a scenario in which signals from an emitter at an unknown location are received at a number of different collinear locations. The receiver can determine the received signal strength, but no other parameters of the signal. Postulating a log-normal transmission model with a constant but unknown path loss exponent and, also, an unknown transmit power and known noise variance, the paper shows how the localization problem can be solved, along with estimating the parameters appearing in the log-normal transmission model, given enough measurements at different points. The log-normal transmission model parameters can be determined first. An algorithm based on construction of a Gram matrix is proposed to estimate the path loss exponent and transmit power parameters from the received noisy power measurements. Since the estimated parameters are biased due to the nonlinearity of the model and constraints, a pattern-matching algorithm is also proposed to remove the bias in the estimates. The distances corresponding to the different received signal strength measurements can then be bounded, and finally the location estimation is formulated as a convex optimization problem where the estimated distances are used as the new measurements. Simulation results are finally provided to assess the efficacy of the proposed methods in the parameter and location estimation. [Copyright &y& Elsevier]

Abstract: In this paper, an automatic optical inspection system for the 3D surface profile of an unsymmetrical microstructure using Fizeau interferometer was proposed. This non-contact optical inspection system is suitable for measuring the lens sag and 3D surface profile of symmetrical and unsymmetrical microlenses. Referring to the unsymmetrical microlenses as an example, the distribution of the interference fringes is partly dense and partly rare, and is completely different from the equally dense distribution of symmetrical microlenses. Thus, a novel algorithm is proposed to solve the above mentioned problem in this paper, namely, individually determining the darkest points of the dark fringes and the brightest points of the bright fringes, and fitting these discrete points as close curves through the Bezier curve theory. As the contour lines of an unsymmetrical microlens are obtained, the 3D surface profile of the unsymmetrical microlens can be plotted correspondingly. Furthermore, the proposed system has the following advantages due to its non-contact structure. This system is specifically designed for in-line measurements according to the rapid inspection speed; it has no need to coat a reflective layer on the inspected microstructure, thus avoiding damaging the surface structure of the sample. [Copyright &y& Elsevier]

Abstract: Distribution network reconfiguration problem is simply aimed at finding the best set of radial configurations among a huge number of possibilities. Each solution of this set ensures optimal operation of the system without violating any prescribed constraint. To solve such problem, it is important to count on efficient procedure to enforce radiality. In this paper, we propose a reliable approach to deal properly with topology constraint enabling algorithm convergence toward optimal or quasi-optimal solutions. A simple and practical codification of individuals used in evolutionary algorithms to solve the generalized reconfiguration problem is detailed in this paper. Mathematical formulation, algorithm, and simulation results are presented for the distribution reconfiguration problem, incorporating a new representation scheme which is immune to topologically unfeasible possibilities. The individual interpretation procedure is straight and it demands no additional data structure or graph preprocessing. Comparisons are made for five well-known distribution systems to demonstrate the efficacy of the proposed methodology. It is also demonstrated that optimal configurations are properly surveyed when single or multiple sources are dealt. [Copyright &y& Elsevier]

Abstract: In this paper, we concentrate on fast removing speckle noise in real ultrasound images. It is hard to design a fast algorithm to solve a speckle reduction model, since the data fitting term in a speckle reduction model is usually not convex. In this paper, we present a convex variational model to deal with speckle noise in real ultrasound images. The data-fitting term of the proposed model is obtained by using a generalized Kullback–Leibler distance. To fast solve the proposed model, we incorporate variable splitting method and Bregman iterative method to propose a fast ultrasound speckle reduction algorithm. The capability of the proposed method is shown both on synthetic images and real ultrasound images. [Copyright &y& Elsevier]

Abstract: This paper addresses constrained and optimal engineering problems solved using an adapted particle swarm optimization (PSO) algorithm. In fact, a specific constraint-handling mechanism is presented. It consists of a closeness evaluation of the solutions to the feasible region. The total constraints violation is introduced as an objective function to minimize. Interval arithmetic is used to normalize the total violations. The resulting objective problem is solved using a simple lexicographic method. The new algorithm is called CVI-PSO for constraint violation with interval arithmetic PSO. The paper provides numerous experimental results based on a well-known benchmark and comparisons with previously reported results. Finally, a case study of the optimal design of an electrical actuator with several model reformulations is detailed. [Copyright &y& Elsevier]

Abstract: This paper presents two direct explicit methods of computer-aided design for developable surfaces. The developable surfaces are designed by using control planes with C-Bézier basis functions. The shape of developable surfaces can be adjusted by using a control parameter. When the parameter takes on different values, a family of developable surfaces can be constructed and they keep the characteristics of Bézier surfaces. The thesis also discusses the properties of designed developable surfaces and presents geometric construction algorithms, including the de Casteljau algorithm, the Farin–Boehm construction for G 2 continuity, and the G 2 Beta restricted condition algorithm. The techniques for the geometric design of developable surfaces in this paper have all the characteristics of existing approaches for curves design, but go beyond the limitations of traditional approaches in designing developable surfaces and resolve problems frequently encountered in engineering by adjusting the position and shape of developable surfaces. [Copyright &y& Elsevier]

Abstract: In this paper, we study the geodetic convexity of graphs, focusing on the problem of the complexity of computing a minimum hull set of a graph in several graph classes. For any two vertices of a connected graph , the closed interval of and is the set of vertices that belong to some shortest -path. For any , let . A subset is geodesically convex or convex if . In other words, a subset is convex if, for any and for any shortest -path , . Given a subset , the convex hull of is the smallest convex set that contains . We say that is a hull set of if . The size of a minimum hull set of is the hull number of , denoted by . The Hull Number problem is to decide whether , for a given graph and an integer . Dourado et al. showed that this problem is NP-complete in general graphs. In this paper, we answer an open question of Dourado et al. (2009) [1] by showing that the Hull Number problem is NP-hard even when restricted to the class of bipartite graphs. Then, we design polynomial-time algorithms to solve the Hull Number problem in several graph classes. First, we deal with the class of complements of bipartite graphs. Then, we generalize some results in Araujo et al. (2011) [2] to the class of -graphs and to cacti. Finally, we prove tight upper bounds on the hull numbers. In particular, we show that the hull number of an -node graph without simplicial vertices is at most in general, at most if is regular or has no triangle, and at most if has girth at least . [Copyright &y& Elsevier]

Abstract: This paper deals with the problem of observer design for a class of implicit nonlinear systems of index one. To solve this problem, an implicit observer which is given by an implicit system is usually used. The implementation of this kind of observer requires an optimization algorithm to solve at each time the algebraic constraints. Alternatively, an explicit observer which is described by an ordinary differential equation can be used. The advantage of this approach is that no optimization algorithm is required. Moreover the observer initialization may be done outside the constraints. In this paper it is shown that, under some conditions, the existence of an implicit observer implies the existence of an explicit one. Then, an explicit observer is given for implicit state affine systems whose dynamic involves an additional nonlinear term depending on the input, the output and the implicit variable. [Copyright &y& Elsevier]

Abstract: This paper presents an innovative Tribe-Modified Differential Evolution (Tribe-MDE) for solving multi-objective Environmental/Economic Dispatch (EED) problems. By using this method the multi-objective problem will be changed into a mini-max problem on this first stage and then will be solved using the Tirbe-MDE algorithm. The operator, a person who is competent with respect to the problem, can initiate all the necessary actions to determine an optimal solution. Because of the fact that all of Pareto obtained solutions are of the same level of preference an operator will be able to affect his/her opinions with respect to different conflicting objectives. Using this methodology the operator can achieve the optimal solution. The DE algorithm is an advanced stochastic method that can prepare the necessary preliminaries for an operator to solve the EED problem. DE method has several advantages including its few control variables, local searching capability, fast results, easy using process and simple structure and using the control variables logically has a significant effect on the results. The subject of this paper is studying the modified DE algorithm which is founded on self-adaptive control. In this method a diversity-preserving method is used to solve the premature convergence problem and for proving the effectiveness of this method in solving EED problems the presented algorithms is applied on different systems. [Copyright &y& Elsevier]

Abstract: This paper studied region-of-interest (ROI) problem of computed tomography. It is necessary to use almost local projection data to reduce radiation exposure or time in medical imaging when only a small part of the patient''s body needs to be viewed. Improving the quality of reconstructed ROI and reducing radiation exposure are our aims. However, the traditional local tomography algorithm has difficulty in reconstructing the ROI due to significant truncation artifacts. In this paper, a new grey model based method is reported for ROI image reconstruction from truncated projection data. By using grey model, the proposed method can extrapolate the truncated projection data, and reconstruct the ROI from a set of its projections. As a result, about 75% of full projection data are saved, as compared with other traditional approachs, in reconstructing a local region of 32 pixels in radius in an image of 256×256 pixels. Experimental results show that the proposed method exhibits more saving in exposure as compared with other local tomography algorithms and results in better quality of the reconstructed image. [Copyright &y& Elsevier]

In Shu and Lin, 2011, exponential time (actually polynomial space) algorithms for determining the weak detectability and weak periodic detectability of nondeterministic discrete event systems (DESs) are designed. In this paper, we prove that the problems of determining the weak detectability and weak periodic detectability of deterministic DESs are both PSPACE -hard (hence PSPACE -complete). Then as corollaries, the problems of determining the weak detectability and weak periodic detectability of nondeterministic DESs are also PSPACE -hard (hence PSPACE -complete). [ABSTRACT FROM AUTHOR]

Graph editing problems offer an interesting perspective on sub- and supergraph identification problems for a large variety of target properties. They have also attracted significant attention in recent years, particularly in the area of parameterized complexity as the problems have rich parameter ecologies. In this paper we examine generalisations of the notion of editing a graph to obtain a regular subgraph. In particular we extend the notion of regularity to include two variants of edge-regularity along with the unifying constraint of strong regularity. We present a number of results, with the central observation that these problems retain the general complexity profile of their regularity-based inspiration: when the number of edits k and the maximum degree r are taken together as a combined parameter, the problems are tractable (i.e. in FPT ), but are otherwise intractable. We also examine variants of the basic editing to obtain a regular subgraph problem from the perspective of parameterizing by the treewidth of the input graph. In this case the treewidth of the input graph essentially becomes a limiting parameter on the natural k + r parameterization. [ABSTRACT FROM AUTHOR]