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In industries which employ large numbers of mobile field engineers (resources), there is a need to optimize the task allocation process. This particularly applies to utility companies such as electricity, gas and water suppliers as well as telecommunications. The process of allocating tasks to engineers involves finding the optimum area for each engineer to operate within where the locations available to the engineers depends on the work area she/he is assigned to. This particular process is termed as work area optimization and it is a sub-domain of workforce optimization. The optimization of resource scheduling, specifically the work area in this instance, in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. In previous attempts to tackle workforce optimization in real world scenarios, single objective optimization algorithms employing crisp logic were employed. The problem is that there are usually many objectives that need to be satisfied and hence multi-objective based optimization methods will be more suitable. Type-2 fuzzy logic systems could also be employed as they are able to handle the high level of uncertainties associated with the dynamic and changing real world workforce optimization and scheduling problems. This paper presents a novel multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization, which was employed in real world scheduling problems. This system had to overcome challenges, like how working areas were constructed, how teams were generated for each new area and how to realistically evaluate the newly suggested working areas. These problems were overcome by a novel neighborhood based clustering algorithm, sorting team members by skill, location and effect, and by creating an evaluation simulation that could accurately assess working areas by simulating one day's worth of work, for each engineer in the working area, while taking into account uncertainties. The results show strong improvements when the proposed system was applied to the work area optimization problem, compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities and telecommunications companies. [ABSTRACT FROM AUTHOR]

Bacterial foraging optimization (BFO) inspired by a behavior of bacteria called chemotaxis is a novel stochastic optimization algorithm, its chemotactic movement mimics a trial solution through random search directions. However, it may enable BFO to possess a poor optimizing performance as compared to other optimization methods over complex optimization problems. To improve the exploration and exploitation abilities of the standard BFO, this paper proposes an effective bacterial foraging optimization (EBFO). First a gravitational search strategy is incorporated into the chemotaxis step to adjust its unit length according to the swarm information. Then, a swarm diversity strategy is integrated into the reproduction step to enhance the reproduction mode depending on the swarm diversity. We evaluate the performance of the EBFO on 23 numerical benchmark functions, then it is applied to identifying parameters of a chaotic system. The simulation results show that the proposed algorithm is more effective than its competitors and can be extended to other global optimization problems. [ABSTRACT FROM AUTHOR]

When all training instances and labels are considered all together in a single optimization problem, multi-label support and core vector machines (i.e., Rank-SVM and Rank-CVM) are formulated as quadratic programming (QP) problems with equality and bounded constraints, whose training procedures have a sub-linear convergence rate. Therefore it is highly desirable to design and implement a novel efficient SVM-type multi-label algorithm. In this paper, through applying pairwise constraints between relevant and irrelevant labels, and defining an approximate ranking loss, we generalize binary Lagrangian support vector machine (LSVM) to construct its multi-label form (Rank-LSVM), resulting into a strictly convex QP problem with non-negative constraints only. Particularly, each training instance is associated with a block of variables and all variables are divided naturally into manageable blocks. Consequently we build an efficient training procedure for Rank-LSVM using random block coordinate descent method with a linear convergence rate. Moreover a heuristic strategy is applied to reduce the number of support vectors. Experimental results on twelve data sets demonstrate that our method works better according to five performance measures, and averagely runs 15 and 107 times faster and has 9 and 15% fewer support vectors, compared with Rank-CVM and Rank-SVM. [ABSTRACT FROM AUTHOR]

In the past decades, different evolutionary optimization methodologies have been proposed by scholars and exploited by practitioners, in a wide range of applications. Each paradigm shows distinctive features, typical advantages, and characteristic disadvantages; however, one single problem is shared by almost all of them: the “lack of speciation”. While natural selection favors variations toward greater divergence, in artificial evolution candidate solutions do homologize. Many authors argued that promoting diversity would be beneficial in evolutionary optimization processes, and that it could help avoiding premature convergence on sub-optimal solutions. The paper surveys the research in this area up to mid 2010s, it re-orders and re-interprets different methodologies into a single framework, and proposes a novel three-axis taxonomy. Its goal is to provide the reader with a unifying view of the many contributions in this important corpus, allowing comparisons and informed choices. Characteristics of the different techniques are discussed, and similarities are highlighted; practical ways to measure and promote diversity are also suggested. [ABSTRACT FROM AUTHOR]

This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach employs a single reference point to express the preferences of a decision maker, and adaptively biases the search procedure toward the region of the Pareto-optimal front that best matches its expectations. Experimental results suggest that incorporating preferences within these algorithms leads to improvements in several quality criteria, and that the proposed approach is capable of yielding competitive results when compared against existing algorithms. [ABSTRACT FROM AUTHOR]

Consistency of preference relations is related to rationality, the lack of consistency in decision making often leads to misleading solutions. This paper studies the ordinal and cardinal consistency problems of fuzzy reciprocal preference relations. First, the concept of ordinal consistency of fuzzy reciprocal preference relations is introduced and analyzed. A l p distance-based method is proposed to formulate the underlying optimization problems as goal programming (GP) models for ordinal and additive consistency problems respectively. By setting different p , three GP models are obtained: (a) a linear GP model where p = 1; (b) a MINMAX GP model, where p = ∞; and (c) an extended GP model, which integrates the two previous models in particular cases. Utilizing these models, we can solve the ordinal and additive consistency problems for fuzzy reciprocal preference relations. The proposed model can preserve the initial preference information as much as possible. Finally, a numerical example and comparative analysis are provided to show effectiveness and validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a basic variable neighborhood search for solving Minimum differential dispersion problem using only the swap neighborhood structure in both descent (intensification) and shaking (diversification) steps. It has become a trend in the metaheuristic literature to use hybrid metaheuristics, i.e., combination of several metaheuristic paradigms, for solving some particular optimization problem. We show that our simple method, which relies on the basic Variable neighborhood search, significantly outperforms the hybrid one that combines GRASP, Variable neighborhood search, and Exterior path relinking metaheuristics. Thus, simplicity is not only the desired user friendly property of a heuristic but can lead to more efficient and effective method than if complex hybrid metaheuristic is used: less is more. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven dynamic modeling method for the multivariate prediction of molten iron quality (MIQ) in a blast furnace (BF) using online sequential random vector functional-link networks (OS-RVFLNs) with the help of principal component analysis (PCA). At first, a data-driven PCA is employed to identify the most influential components from multitudinous factors that affect MIQ so as to reduce the model dimension. Secondly, a dynamic OS-RVFLNs modeling technology with fast learning and strong nonlinear mapping capability is proposed by applying the output self-feedback structure to the traditional OS-RVFLNs. Since it has been shown that such a dynamic modeling method has the ability to store and handle input-output data at different time scales, the dynamic OS-RVFLNs based MIQ prediction model has exhibited the potential for multivariable nonlinear mapping and the adaptability to dynamic time-varying process. Finally, some industrial experiments and comparative studies have been carried out on the 2# BF in Liuzhou Iron & Steel Group Co. of China using the proposed method, where it has been demonstrated that the constructed model produces a better modeling and estimating accuracy and has faster learning speed than other conventional MIQ modeling methods. [ABSTRACT FROM AUTHOR]

Users of web search engines are increasingly worried that their query activities may expose what topics they are interested in, and in turn, compromise their privacy. It would be desirable for a search engine to protect the true query intention for users without compromising the precision-recall performance. In this paper, we propose a client-based approach to address this problem. The basic idea is to issue plausible but innocuous pseudo queries together with a user query, so as to mask the user intention. First, we present a privacy model which formulates plausibility and innocuousness, and then the requirements which should be satisfied to ensure that the user intention is protected against a search engine effectively. Second, based on a semantic reference space derived from Wikipedia, we propose an approach to construct a group of pseudo queries that exhibit similar characteristic distribution as a given user query, but point to irrelevant topics, so as to meet the security requirements defined by the privacy model. Finally, we conduct extensive experimental evaluations to demonstrate the practicality and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Denoeux proposed an operator called the cautious conjunctive rule (CCR) to combine non-dogmatic belief functions (i.e., the frame is considered a focal element) from reliable dependent sources, which can occur in practice. However, in cases such as uncertain multiple attribute and group decision analyses, belief functions may denote data that vary in importance. This paper extends CCR as a weighted CCR (WCCR) to combine belief functions in consideration of their relative weights in such situations. Properties of WCCR are analyzed, proven, and demonstrated using numerical examples. In particular, the consideration of relative weights enables WCCR to combine dogmatic belief functions. The normalized WCCR is further constructed and used to assess the trustworthiness of a hospital information system employed in many hospitals in Anhui Province, China. [ABSTRACT FROM AUTHOR]

Unlike the widely existing H 2 / H ∞ filtering methods under single (i.e., wired or wireless) network with single channel environment, there exist multiple event-generators and different network-induced delays in hybrid wired–wireless networks, which make H 2 / H ∞ filtering analysis and design more complex. The main objective of this paper is to investigate multiple event-triggered H 2 / H ∞ filtering for hybrid wired–wireless networked systems with random network-induced delays. For multi-sensor network communications, a multiple event-triggered mechanism is employed firstly, which can reduce communication burden of each channel. The different communication characters of hybrid wired–wireless networks are then described by two Markov chains, and a general filtering error dynamic system model with the event-triggered parameters and multi-channel network-induced delays of hybrid wired–wireless networks is presented. Furthermore, the designed filter enables the filtering error dynamic system to be stochastic stability and to achieve a prescribed performance, and the relationships between the stability criteria and the maximum network-induced delays of hybrid wired–wireless networks, the event-triggered parameters and the system performance parameter are established. Finally, simulation results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Considering the practical application background in peer-to-peer network system, we investigate the single-variable term semi-latticized fuzzy relation geometric programming with max-product operator in this paper. Based on the characteristics of the objective function and feasible domain, we develop a matrix approach to deal with the proposed problem. A step-by-step algorithm is proposed to find an optimal solution without solving all the minimal solutions of the constraint. Two examples are given to illustrate the feasibility and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the notions of α -migrative uninorms over semi t-operators and α -migrative semi t-operators over uninorms are introduced and investigated. All solutions of the migrativity equations for all possible combinations of semi t-operators and uninorms are characterized. [ABSTRACT FROM AUTHOR]

Given a bounded lattice L with bounds 0 and 1, it is well known that the set Pol 0 , 1 ( L ) of all 0, 1-preserving polynomials of L forms a natural subclass of the set C ( L ) of aggregation functions on L . The main aim of this paper is to characterize all finite lattices L for which these two classes coincide, i.e. when the set C ( L ) is as small as possible. These lattices are shown to be completely determined by their tolerances, also several sufficient purely lattice-theoretical conditions are presented. In particular, all simple relatively complemented lattices or simple lattices for which the join (meet) of atoms (coatoms) is 1 (0) are of this kind. [ABSTRACT FROM AUTHOR]

In order to attain better reconstruction quality from compressive sensing (CS) of images, exploitation of the dependency or correlation patterns among the transform coefficients commonly has been employed. In this paper, we study a new image sensing technique, called compressive image sensing (CIS), with computational complexity O ( m 2 ), where m denotes the length of a measurement vector y = ϕ x , which is sampled from the signal x of length n via the sampling matrix ϕ with dimensionality m × n . CIS is basically a variation on compressive sampling. The contributions of CIS include: (i) reconstruction speed is extremely fast due to a closed-form solution being derived; (ii) certain reconstruction accuracy is preserved because significant components of x can be reconstructed with higher priority via an elaborately designed ϕ ; and (iii) in addition to conventional 1D sensing, we also study 2D separate sensing to enable simultaneous acquisition and compression of large-sized images. [ABSTRACT FROM AUTHOR]

The diversity of solutions is of great importance for multi-objective evolutionary algorithms. In this paper, a new multi-objective particle swarm optimization algorithm based on decomposition (MPSO/D) is proposed. Firstly, the objective space of a multi-objective problem is decomposed into a set of sub-regions based on a set of direction vectors. Then MPSO/D makes each sub-region have a solution to maintain the diversity. Secondly, considering the convergence of solutions, MPSO/D uses the crowding distance to calculate the fitness values of the reserved solutions for selection operator, and uses the neighboring particles of a particle to determine the global best historical position ( gbest ) of the particle. The proposed algorithm has been compared with NSGAII, MOEA/D and NNIA on sixteen test sets. The experimental results illustrate that the proposed algorithm outperforms NSGAII, MOEA/D and NNIA in terms of convergence and diversity. [ABSTRACT FROM AUTHOR]

Graph construction from data constitutes a pre-stage in many machine learning and computer vision tasks, like semi-supervised learning, manifold learning, and spectral clustering. The influence of graph construction procedures on learning tasks and their related applications has only received limited study despite its critical impact on accuracy. State-of-the-art graphs are built via sparse coding adopting ℓ 1 regularization. Those graphs exhibit good performance in many computer vision applications. However, the locality and similarity among instances are not explicitly used in the coding scheme. Furthermore, due to the use of ℓ 1 regularization, these construction approaches can be computationally expensive. In this paper, we investigate graph construction using the data self-representativeness property. By incorporating a variant of locality-constrained linear coding (LLC), we introduce and derive four variants for graph construction. These variants adopt a two phase LLC (TPLLC). Compared with the recent ℓ 1 graphs, our proposed objective function, associated with three variants, has an analytical solution, and thus, is more efficient. A key element of the proposed methods is the second phase of coding that allows data closeness, or locality, to be naturally incorporated. It performs a coding over some selected relevant samples and reinforces the individual regularization terms by exploiting the coefficients estimated in the first phase. Comprehensive experimental results using several benchmark datasets show that it can achieve or outperform existing state-of-the-art results. Furthermore, it is shown to be more efficient than the robust ℓ 1 graph construction schemes. [ABSTRACT FROM AUTHOR]

Nullnorms are generalization of triangular norms and triangular conorms with a zero element in the interior of the unit interval. In this paper, we generalize nullnorms to an arbitrary bounded lattices. We show the existence of nullnorms with zero element a for arbitrary element a ∈ L \{0, 1} using the fact that some t-norms and t-conorms on an arbitrary fixed bounded lattice L always exist. As a by-product we give a method of constructing nullnorms. Using this method, we obtain also the smallest nullnorm and the greatest nullnorm with given zero element a . Some other construction methods for nullnorms are also added. [ABSTRACT FROM AUTHOR]

To avoid traffic accidents, drivers must constantly be aware of nearby vehicles. Unfortunately, nearby vehicles often go unnoticed because of various obstacles such as other vehicles, buildings, or poor weather. In this paper, we study Moving range k -nearest neighbor (MR k NN) queries as a tool for continuously monitoring nearby moving objects. A simple approach to processing MR k NN queries is to have each object periodically broadcast information regarding its movements (i.e., location and velocity at a particular time) to other objects. However, this simple technique cannot be used to process MR k NN queries owing to the limited network bandwidth in mobile peer-to-peer environments. Therefore, we address this bandwidth limitation by proposing a probabilistic algorithm, called MINT, for M ov I ng range k - N N queries with quali T y guarantee over uncertain moving objects. MINT provides users with approximate answers with a quality guarantee, rather than exact answers, with near optimal communication costs. Using a series of simulations, we demonstrate the efficiency and efficacy of MINT in evaluating MR k NN queries with a quality guarantee. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on ranking values of polygonal fuzzy sets and automatically generated weights of fuzzy rules. First, the proposed method gets the characteristic points of the observation polygonal fuzzy sets and the characteristic points of the antecedent polygonal fuzzy sets of the fuzzy rules, respectively, by using the α-cut operations, respectively, where α ∈ [0, 1]. Then, it calculates the ranking value of each observation polygonal fuzzy set and the ranking value of each antecedent polygonal fuzzy set, respectively. Then, it calculates the difference between the ranking value of each observation polygonal fuzzy set and the ranking value of each antecedent polygonal fuzzy set. Then, it calculates the weight of each fuzzy rule based on the obtained differences between the ranking values of the observation polygonal fuzzy sets and the ranking values of the antecedent polygonal fuzzy sets. Then, based on the obtained characteristic points of the observation polygonal fuzzy sets, the obtained characteristic points of the antecedent polygonal fuzzy sets, and the obtained weights of the fuzzy rules, it gets the characteristic points of the fuzzy interpolative reasoning result represented by a polygonal fuzzy set. The experimental results show that the proposed method can overcome the drawbacks of the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. [ABSTRACT FROM AUTHOR]

Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing them from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that conceptualises the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users relationships are constantly evolving; and the information sharing norms are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks. In this paper, we propose the first computational model of Implicit Contextual Integrity, presenting an information model for Implicit Contextual Integrity as well as a so-called Information Assistant Agent that uses the information model to learn implicit contexts, relationships and the information sharing norms in order to help users avoid inappropriate information exchanges and undesired information disseminations. Through an experimental evaluation, we validate the properties of the model proposed. In particular, Information Assistant Agents are shown to: (i) infer the information sharing norms even if a small proportion of the users follow the norms and in presence of malicious users; (ii) help reduce the exchange of inappropriate information and the dissemination of sensitive information with only a partial view of the system and the information received and sent by their users; and (iii) minimise the burden to the users in terms of raising unnecessary alerts. [ABSTRACT FROM AUTHOR]

Two primary properties of complex networks, controllability and modularity not only are closely related to each other, but also play an important role in understanding the networks’ characteristics. In this paper, we discuss an elastic model to enhance the control of undirected networks and study the controllability of random networks with built-in gradually varied modularity as well as real-world networks to elaborate the correlation between the networks’ controllability and modularity. The results show that it is easier to control the networks with stronger modularity than that of weaker modularity, and the networks with larger size communities probably need more driver nodes to control than that of smaller size communities when we fix both the number of nodes and the number of links of the networks. In addition, the robustness analysis indicates that the model enhances the resistance of networks against link failure. This work shows that the controllability of complex networks is highly associated with the networks’ degree distribution as well as the networks’ modularity, which gives a new insight into the understanding of controllability and modularity of complex networks. [ABSTRACT FROM AUTHOR]

In this paper, we first discuss some properties of residuum and deresiduum of a binary operation on a complete lattice. Then, we investigate the relations between residuum and deresiduum. Finally, we demonstrate how each individual logical character of a right infinitely ∧-distributive implication, such as the law of importation, the weak exchangeability principle, the exchange principle, the contrapositive symmetry and the contraction law, can be translated into a property of its deresiduum. Moreover, we give some conditions under which deresiduum of a right infinitely ∧-distributive implication is, respectively, a left (right) uninorm, pseudo-uninorm and uninorm, and show that right infinitely ∧-distributive implications, which satisfy the weak exchangeability principle and the contrapositive symmetry, can be presented by commutative conjunctors and strong negations. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems. [ABSTRACT FROM AUTHOR]

Measuring the similarity between two instances reliably, shape or image, is a challenging problem in shape and image retrieval. In this paper, a simple yet effective method called Neighbor Set Similarity (NSS) is proposed, which is superior to both traditional pairwise similarity and diffusion process. NSS makes full use of contextual information to capture the geometry of the underlying manifold, and obtains a more precise measure than the original pairwise similarity. Moreover, based on NSS, we propose a powerful fusion process to utilize the complementarity of different descriptors to further enhance the retrieval performance. The experimental results on MPEG-7 shape dataset, N-S image dataset and ORL face dataset demonstrate the effectiveness of the proposed method. In addition, the time complexity of NSS is much lower than diffusion process, which suggests that NSS is more suitable for large scale image retrieval than diffusion process. [ABSTRACT FROM AUTHOR]

In this paper, we propose two novel methods viz., counterpropagation auto-associative neural network (CPAANN) and grey system theory (GST) hybridised with CPAANN for data imputation. The effectiveness of these methods is demonstrated on 12 datasets and the results are compared with that of various extant methods. Wilcoxon signed rank test conducted at 1% level of significance, indicated that the proposed methods are statistically significant against all methods. The spectacular success of CPAANN can be attributed to the local learning, global approximation and auto-association that take place in tandem in a single architecture. Furthermore, significantly CPAANN turned out to be the best in the class of AANN architectures used for imputation. The reason could be the competitive learning that is intrinsic to the CPAANN architecture, but conspicuously absent in other auto-associative neural network architectures. [ABSTRACT FROM AUTHOR]

This paper presents two mathematical models representing imprecise capacitated fixed-charge transportation problems for a two-stage supply chain network in Gaussian fuzzy type-2 environment. It is a two-stage transportation process from a manufacturing center to m potential distribution centers (DCs) and then from DCs to business centers of n retailers with particular demands. Retailers are situated at some distances apart. Here unit transportation costs, fixed charges, availabilities, and demands are imprecise and represented by Gaussian type-2 fuzzy numbers. The proposed models are formulated as profit maximization problems in such a way that some DCs are selected in order to satisfy the demands at all retailers. The type-2 fuzziness has been removed by using generalized credibility measure developed with the help of CV-based reduction method and hence the models are reduced to chance constrained programming problems with different credibility labels. The deterministic models are then solved using both genetic algorithm (GA) based on Roulette wheel selection, arithmetic crossover with uniform mutation and modified particle swarm optimization (PSO), where the position of each particle is adjusted according to its own experience and that of its neighbors. Finally models are illustrated with some numerical data. Some sensitivity analyses on the proposed models are presented. [ABSTRACT FROM AUTHOR]

Although various efficient indexing techniques have been proposed to improve the performance in distributing road data to mobile clients to perform shortest path searches on road networks, how to minimize the tune-in and path searching costs are still important performance goals since the clients usually have limited processing capability and energy supply. In this paper, based on the Next Region ( NR ) approach, we propose a new method called Approximate Path Searching (APS) for constructing the broadcast index at mobile clients with soft arrival times to destinations. APS adopts an approximation technique in calculating the sets of required regions for each region pair in a road network to reduce the index generation cost at the road server and the tune-in and path searching costs at the clients. Different from NR , which only provides one set of required regions containing the shortest path from a client’s start region to its destination region, APS generates n -sets of required regions for a client to choose. Each set of required regions contains different numbers of regions. Although choosing a smaller region set may give a slightly longer delay in arrival, the tune-in and path searching costs could be significantly reduced. Theoretical analysis and extensive simulation experiments have been performed to demonstrate the effectiveness of APS in improving the system performance with just a few percentages delay in arrival time on average comparing with NR using data sets from real road networks. [ABSTRACT FROM AUTHOR]

Sparse representation based reconstruction methods are efficient for single image super resolution. They generally consist of the code stage and the linear combination stage. However, the simple linear combination has not considered the image edge constraint information of image, and hence the classical sparse representation based methods reconstruct the image with the unwanted edge artifacts and the unsharp edges. In this paper, considering that the local rank is able to extract better edge information than other edge operator, we propose a new single image super resolution method by combining the sparse representation and the local rank constraint information. In our method, we first learn the local rank of the HR image via the traditional sparse representation model, and then use it as the edge constraint to restrict the image edges during the linear combination stage to reconstruct the HR image. Furthermore, we propose a nonlocal and global optimization model to further improve the HR image quality. Compared with many state-of-art methods, extensive experimental results validate that the proposed method can obtain the less edge artifacts and sharper edges. [ABSTRACT FROM AUTHOR]

This paper presents a proposed new approach for complex control combining several simpler individual fuzzy controllers. This method is particularly useful when the case of study is a multivariable control system. The proposed method has a hierarchical architecture with 2 levels (individual fuzzy systems and a superior control to adjust the global result). The behavior of the proposed method is illustrated with a problem of flight control that requires several individual controllers. In addition a statistical comparison is performed using the t student test, where the proposed control strategy is compared against a simple fuzzy control approach. Finally, an optimization method is also applied to achieve an optimal design of the fuzzy systems. [ABSTRACT FROM AUTHOR]

Differential Evolution is an efficient metaheuristic for continuous optimization that suffers from the curse of dimensionality. A large amount of experimentation has allowed researchers to find several potential weaknesses in Differential Evolution. Some of these weaknesses do not significantly affect its performance when dealing with low-dimensional problems, so the research community has not paid much attention to them. The aim of this paper is to provide a better insight into the reasons of the curse of dimensionality and to propose techniques to alleviate this problem. Two different weaknesses are revisited and schemes for dealing with them are devised. The schemes increase the diversity of trial vectors and improve on the exploration capabilities of Differential Evolution. Some important mathematical properties induced by our proposals are studied and compared against those of related schemes. Experimentation with a set of problems with up to 1000 dimensions and with several variants of Differential Evolution shows that the weaknesses analyzed significantly affect the performance of Differential Evolution when used on high-dimensional optimization problems. The gains of the proposals appear when highly exploitative schemes are used. Our proposals allow for high-quality solutions with small populations, meaning that the most significant advantages emerge when dealing with large-scale optimization problems, where the benefits of using small populations have previously been shown. [ABSTRACT FROM AUTHOR]

The success of kernel-based learning methods is heavily dependent on the choice of a kernel function and proper setting of its parameters. In this paper, we optimize the Gaussian kernel for binary-class problems by using centered kernel polarization criterion. This criterion is an extension of kernel polarization and a simplified style of centered kernel alignment. Compared with formulated kernel polarization criterion, the proposed criterion has a defined geometrical significance, and it can locate the global optimal point with less influence of threshold selection. Furthermore, the approximate criterion function can be proved to have a determined global minimum point by adopting the Euler–Maclaurin formula under weaker conditions. In addition, taking the preservation of within-class local structure into account, we present an evaluation criterion named local multiclass centered kernel polarization in multiclass classification scenario. Comparative experiments are conducted on some benchmark examples with three Gaussian kernel based learning methods and the results well demonstrate the effectiveness and efficiency of the proposed quality measures. [ABSTRACT FROM AUTHOR]

In this paper we study the optimal solutions set for a generalized differentiable interval-valued function. Necessary and sufficient optimality conditions are established for gH-differentiable functions. Convexity assumptions that are necessary or required to ensure the characterization of the optimal solutions are weaker or less strict than those presented in previous works. These convexity assumptions are the weakest to characterize the optimal solutions set. Known results for classical non interval-valued optimization are particular cases of the ones proved here. [ABSTRACT FROM AUTHOR]

Depth map is the basic requirement for all three-dimensional (3D) applications, but facing sensor noises, low frame-rate and low resolution in the procedure of data acquisition, especially in multiview cases. These problems bring obstacles to high quality 3D applications. Among the existing approaches, depth propagation is one of the promise approaches, and it can be utilized in temporal or spatial manner. However, propagation based algorithms process one aspect of the mentioned problems to pursuit local optimal solution. Actually, the process chain of depth map is from capture to application, and the optimization should be coupled instead of mutually independent. In this paper, we proposed a bundled-optimization scheme to process the thorough chain from capture to multiview dense depth map generation for the 3D applications. In this scheme, sensor noises in the captured low-resolution depth map are first detected and removed through a frequency-counting based non-linear filter. The filter refrains from the noise amplification in the procedure of depth map up-sampling. Low-pass blurring effect around high frequency areas is the by-product in up-sampling, and it is hard to detect in the depth map. We therefore propose a Blocklet based depth map optimization method for this blurring effect, and the accuracy of the high resolution depth map is then improved. Temporal depth propagation is then utilized on the optimized depth maps through the optical flow field rectified by temporal and spatial constrains. After that, a multi-set graph cut model is proposed to synthesize the multiview dense depth map. The experimental results indicate that our scheme can achieve at least 13.2575% PSNR gains when comparing to the benchmark depth map synthesis methods, and suggest the effectiveness of the proposed bundled-optimization method. [ABSTRACT FROM AUTHOR]

This paper presents a novel meta-heuristic optimization algorithm, named Adaptive Simplified Human Learning Optimization (ASHLO), which is inspired by the human learning mechanisms. Three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to generate new solutions and search for the optima by mimicking the learning behaviors of humans. The numerical functions, deceptive functions and 0–1 knapsack problems are adopted as benchmark problems to validate the performance of ASHLO, and the results are compared with those of binary particle swarm optimization (BPSO), modified binary differential evolution (MBDE), the binary fruit fly optimization algorithm (bFOA) and adaptive binary harmony search (ABHS). The experimental results demonstrate that the developed ASHLO significantly outperforms BPSO, MBDE, bFOA and ABHS and has a robust search ability for various problems. With the adaptive strategy, the search ability of ASHLO is improved further especially on the high-dimensional and complicated problems. Considering the ease of implementation, the excellence of global search ability and the robustness for various problems, ASHLO is a promising optimization tool for scientific research and engineering applications. [ABSTRACT FROM AUTHOR]

To achieve high energy efficiency and low response time in big data stream computing environments, it is required to model an energy-efficient resource scheduling and optimization framework. In this paper, we propose a real-time and energy-efficient resource scheduling and optimization framework, termed the Re-Stream. Firstly, the Re-Stream profiles a mathematical relationship among energy consumption, response time, and resource utilization, and obtains the conditions to meet high energy efficiency and low response time. Secondly, a data stream graph is modeled by using the distributed stream computing theories, which identifies the critical path within the data stream graph. Such a methodology aids in calculating the energy consumption of a resource allocation scheme for a data stream graph at a given data stream speed. Thirdly, the Re-Stream allocates tasks by utilizing an energy-efficient heuristic and a critical path scheduling mechanism subject to the architectural requirements. This is done to optimize the scheduling mechanism online by reallocating the critical vertices on the critical path of a data stream graph to minimize the response time and system fluctuations. Moreover, the Re-Stream consolidates the non-critical vertices on the non-critical path so as to improve energy efficiency. We evaluate the Re-Stream to measure energy efficiency and response time for big data stream computing environments. The experimental results demonstrate that the Re-Stream has the ability to improve energy efficiency of a big data stream computing system, and to reduce average response time. The Re-Stream provides an elegant trade-off between increased energy efficiency and decreased response time effectively within big data stream computing environments. [ABSTRACT FROM AUTHOR]

The problem of conditional distributivity for different two aggregation operators plays an important role in many different fields such as utility theory and integration theory. This paper deals with the conditional distributivity of a continuous nullnorm with respect to a uninorm with the continuous underlying t-norm and t-conorm. [ABSTRACT FROM AUTHOR]

A bipartite network is an important type of complex network in human social activities. Newman defined modularity as a measurement for evaluating community structure in unipartite networks. Due to the success of modularity in unipartite networks, bipartite modularities were developed according to different understandings of community in bipartite networks. However, these modularity measurements are subject to resolution limits that could reduce the quality of community partitioning. These modularity measurements contain an intrinsic scale that depends on the total size of links and ignores the number of nodes in a bipartite network. In this paper, we first illustrate such resolution limits of traditional bipartite modularities using several examples of bipartite networks. Next, we propose a quantitative measurement called density-based modularity to evaluate community partitioning in bipartite networks. We verify that optimization of the density-based modularity proposed has no resolution limit. By optimizing this density-based modularity, we can partition the network into the appropriate communities. Experiments on synthetic and real-world bipartite networks verify the accuracy and reliability of our bipartite density-based modularity. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to solving the optimization task that arises when L2-SVM in its primal form is considered. In particular, we propose the application of a Barzilai–Borwein (BB) update step in five variants for the classic Stochastic Gradient Descent (SGD) algorithm. The evaluation is designed to check the effectiveness of the proposed methods in large scale scenarios in terms of execution time, convergence and sensitivity to the choice of initial parameters. The obtained results are compared with those obtained for well-known linear SVM algorithms and they indicate that the level of convergence of the proposed methods is very similar to that found in the other studies. Moreover, our approach shows much lower sensitivity to the choice of initial parameters, which allows for a substantial reduction of pre-processing. [ABSTRACT FROM AUTHOR]

Cooperative Coevolutionary algorithms (CC) have been successful in solving large scale optimization problems. The performance of CC can be improved by decreasing the number of interdependent variables among decomposed subproblems. This is achieved by first identifying dependent variables, and by then grouping them in common subproblems. This approach has potential because so far no grouping technique has been mainly developed for constrained problems. In this paper, a new variable interaction identification technique to identify the dependent variables in large scale constrained problems is proposed. The proposed technique is tested on both a new test suite of constrained problems with medium and high dimensions, which include overlapping subproblems and different levels of complexity and nonseparability and also the established DED problem. The experimental results have shown that the proposed technique contributes to the decomposition approach over a range of high dimensions, in comparison with other state-of-the art grouping techniques. It achieves better performance with higher feasibility ratios and less computational time. [ABSTRACT FROM AUTHOR]

Recent years have witnessed unprecedented volumes of structured data published in RDF format. Taking full advantage of such data has attracted a growing amount of research interest from both academia and industry. However, efficient processing of top- k queries in RDF data is still a new topic. Most existing approaches ignore top- k queries, or only provide a limited number of ranking functions. In this paper, we provide an effective and efficient processing algorithm for top- k queries that consists of a novel tree-style index MS-tree and MS-tree-based filtering and pattern-matching functions. Firstly, candidate entities in RDF data were efficiently ranked and filtered through an MS-tree-based top-down method. Then, query structure patterns are matched in RDF data through graphic exploration. In order to handle more complex scoring functions, a dynamic variable selecting optimization is employed to accelerate the threshold decrease. We evaluate our solutions with both synthetic and real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Hyper-heuristics aim to automate the heuristic selection process in order to operate well across different problem instances, or even across different problem domains. A traditional hyper-heuristic framework has two levels, a high level strategy and a set of low level heuristics. The role of the high level strategy is to decide which low level heuristic should be executed at the current decision point. This paper proposes a Monte Carlo tree search hyper-heuristic framework. We model the search space of the low level heuristics as a tree and use Monte Carlo tree search to search through the tree in order to identify the best sequence of low level heuristics to be applied to the current state. To improve the effectiveness of the proposed framework, we couple it with a memory mechanism which contains a population of solutions, utilizing different population updating rules. The generality of the proposed framework is demonstrated using the six domains of the hyper-heuristic competition (CHeSC) test suite (boolean satisfiability (MAX-SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing with time windows). The results demonstrate that the proposed hyper-heuristic generalizes well over all six domains and obtains competitive, if not better results, when compared to the best known results that have previously been presented in the scientific literature. [ABSTRACT FROM AUTHOR]

A large number of contemporary research efforts focus on incomplete data streams handling. These efforts, usually, focus on the creation and maintenance of top- k lists useful to provide efficient responses to top- k queries. In case of large volumes of data accumulated at high rates the problem becomes more intense as an efficient method for maintaining the top- k list is considered imperative. In this paper, we focus on the behavior of an Observer Entity (OE) responsible to observe the incoming data and initiate the maintenance process of the top-k list . The maintenance process involves the calculation of a score for each object and the update of the top- k list. We adopt the principles of Optimal Stopping Theory (OST) and introduce a scheme that determines, in the appropriate time, when the OE decides to initiate the maintenance process. Due to the incomplete data setting, the high rate of the incoming data and the large volume of data, the maintenance process is initiated only when the OE has the appropriate amount of information for providing a top- k list. In contrast to other research approaches, we do not maintain any additional sets of objects. Instead, we attempt to minimize the number of the necessary processing tasks over the list of objects. We present a mathematical analysis for our scheme and an extensive experimental evaluation. The comparison with other models shows that the proposed model provides efficiency in the list management and minimizes the required time to result the final top- k list. [ABSTRACT FROM AUTHOR]

This paper presents a method for endowing the generalized interval space with some different structures, such as vector spaces, order relations and an algebraic calculus. With these concepts we formulate interval optimization problems and relate them to classic multi-objective optimization problems. We also present a version of the Von Neumann’s Mini-max Theorem in the interval context. [ABSTRACT FROM AUTHOR]

Frequent subtree mining is a major research topic in knowledge discovery from tree-structured data, whose importance is witnessed by the pervasiveness of such data in several domains. In this paper, we present a novel approach to discover all the frequent ordered subtrees in a tree-structured database. A key aspect is that the structural aspects of the input tree instances are extracted to generate a transactional format that enables the application of standard itemset mining techniques. In this way, the expensive process of subtree enumeration is avoided, while subtrees can be reconstructed in a post-processing stage. As a result, more structurally complex tree data can be handled and much lower support thresholds can be used. In addition to discovering traditional subtrees, this is the first approach to frequent subtree mining that can discover position-constrained subtrees. Each node in the position-constrained subtree is annotated with its exact occurrence and level of embedding in the original database tree. Also, disconnected subtree associations can be represented via virtual connecting nodes. Experiments conducted on synthetic and real-world datasets confirm the expected advantages of our approach over competing methods in terms of efficiency, mining capabilities, and informativeness of the extracted patterns. [ABSTRACT FROM AUTHOR]

Reliability engineers often try to achieve a high reliability level of electronic systems. Reliability optimization problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost, weight. The reliability optimization has been an active area of research for the past decades. In this paper, an improved novel global harmony search (INGHS) approach is proposed to solve reliability optimization problem. The INGHS employs a stochastic position updating strategy to replace the position updating of NGHS, and thus the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. Moreover, a new constraint relaxation method also is integrated into the proposed approach. Applying the proposed approach to several well-known benchmark problems, results indicate that the INGHS algorithm can find better solution effectively and efficiently. Furthermore, the INGHS algorithm is used to solve reliability optimization problems, numerical results demonstrated that the INGHS algorithm performs better than several state-of-the-art algorithms in most cases. [ABSTRACT FROM AUTHOR]

In this paper, we develop a parallel-structured real-coded genetic algorithm (RCGA), named the RGA-RDD, for numerical optimization. Technically, the proposed RGA-RDD integrates three specially designed evolutionary operators – the Ranking Selection (RS), Direction-Based Crossover (DBX), and the Dynamic Random Mutation (DRM) – as a whole to mimic a specific evolutionary process. Unlike the conventional RCGAs that perform evolutionary operators in a series framework, the RGA-RDD embeds a coordinator in the inner parallel loop to organize the operations of the DBX and DRM so that a higher possibility of locating the global optimum is ensured. Besides, based on the results of a systematic parametric analysis, we provide a parameter selection guideline for the settings of the proposed RGA-RDD. Furthermore, a data-driven optimization scheme, which incorporates the uniform design for design of experiments and a shape-tunable neural network for auxiliary decision support, is applied to search for an optimal set of the algorithm parameters. The effectiveness and applicability of the proposed RGA-RDD are demonstrated through a variety of benchmarked optimization problems, followed by comprehensive comparisons with some existing state-of-the-art evolutionary algorithms. Extensive simulation results reveal that the performance of the proposed RGA-RDD is superior to comparative methods in locating the global optimum for real-parameter optimization problems, especially for unsolved multimodal and high-dimensional hybrid functions. [ABSTRACT FROM AUTHOR]

This paper aims to develop distributed learning algorithms for Random Vector Functional-Link (RVFL) networks, where training data is distributed under a decentralized information structure. Two algorithms are proposed by using Decentralized Average Consensus (DAC) and Alternating Direction Method of Multipliers (ADMM) strategies, respectively. These algorithms work in a fully distributed fashion and have no requirement on coordination from a central agent during the learning process. For distributed learning, the goal is to build a common learner model which optimizes the system performance over the whole set of local data. In this work, it is assumed that all stations know the initial weights of the input layer, the output weights of local RVFL networks can be shared through communication channels among neighboring nodes only, and local datasets are blocked strictly. The proposed learning algorithms are evaluated over five benchmark datasets. Experimental results with comparisons show that the DAC-based learning algorithm performs favorably in terms of effectiveness, efficiency and computational complexity, followed by the ADMM-based learning algorithm with promising accuracy but higher computational burden. [ABSTRACT FROM AUTHOR]

Community detection gives us a new way to understand the organization characteristics of complex systems. However, for simplicity, most methods for community detection always model the systems as an undirected graph. In this paper, we propose a new framework that unifies undirected into directed graphs by a function, which creates a mapping that transforms an undirected graph into its two directed modes for community detection. We take the method of modularity optimization as an example and apply it on our framework to uncover community structure in complex networks. Compared with the original modularity optimization, we find that the modularity optimization on our framework not only achieves better results on the LFR benchmark, but also a more detailed subdivision on the Zachary club network and the American college football network. Based on the results above, we confirm that our framework provides a good platform for community detection. [ABSTRACT FROM AUTHOR]

Dynamic flexible job shop scheduling is of significant importance to the implementation of real-world manufacturing systems. In order to capture the dynamic and multi-objective nature of flexible job shop scheduling, and provide different trade-offs among objectives, this paper develops a multi-objective evolutionary algorithm (MOEA)-based proactive–reactive method. The novelty of our method is that it is able to handle multiple objectives including efficiency and stability simultaneously, adapt to the new environment quickly by incorporating heuristic dynamic optimization strategies, and deal with two scheduling policies of machine assignment and operation sequencing together. Besides, a new mathematical model for the multi-objective dynamic flexible job shop scheduling problem (MODFJSSP) is constructed. With the aim of selecting one solution that fits into the decision maker’s preferences from the trade-off solution set found by MOEA, a dynamic decision making procedure is designed. Experimental results in a simulated dynamic flexible job shop show that our method can achieve much better performances than combinations of existing scheduling rules. Three MOEA-based rescheduling methods are compared. The modified ɛ -MOEA has the best overall performance in dynamic environments, and its computational time is much less than two others (i.e., NSGA-II and SPEA2). Utilities of introducing the stability objective, heuristic initialization strategies and the decision making approach are also validated. [ABSTRACT FROM AUTHOR]