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This paper deals with the optimal formation control problem of multi-agent systems under a prior unknown desired shapes. The objective of this paper is to accomplish a rolling optimal formation of multi-agent systems by incorporating trajectory tracking and energy saving control into a unified framework. First, according to the real-time desired position offset between each follower and the leader, the rolling optimization performance indexes are introduced. Then, a distributed rolling optimization algorithm is proposed to realize the desired formation of multi-agent systems under the minimum energy consumption of agents. Finally, several simulation examples are conducted to illustrate the effectiveness of the proposed design algorithm. [ABSTRACT FROM AUTHOR]

Pareto optimality is the fundamental construct employed to determine whether a given solution to a multi-criteria mathematical optimization model is preferred to another solution. In this paper we describe an approach (Pattern Efficient Set Algorithm – PESA) to generating a pattern-efficient set of non-dominated vectors to a multi-objective optimization problem. Our approach incorporates an optimization model designed to yield certain non-dominated vectors that can fill gaps between already generated non-dominated vectors, providing a way to deal with the adjacency of generated non-dominated vectors and to quantify the gaps between them. We also propose a pseudo-randomized variant of PESA (rPESA) that randomly generates hypothetical bounds for the objective functions and uses them in the optimization model. To test our approach we selected ten problems from the literature, including bi-objective, 3-objective, 5-objective and 10-objective test instances with non-convex, disconnected or continuous Pareto. The inverted generational distance (IGD) and the hyper-volume (HV) are used as performance metrics to measure the quality of the obtained approximations. We also present graphically the numerical results from applying our method. [ABSTRACT FROM AUTHOR]

Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques. [ABSTRACT FROM AUTHOR]

In this paper the comparison of a proposed neural network with generalized type-2 fuzzy weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural network with interval type-2 fuzzy weights (NNIT2FW) is presented. Generalized type-2 fuzzy inference systems are used to obtain the generalized type-2 fuzzy weights and are designed by a strategy of increasing and decreasing an epsilon variable for obtaining the different sizes of the footprint of uncertainty (FOU) for the generalized membership functions. The proposed method is based on recent approaches that handle weight adaptation using type-1 and type-2 fuzzy logic. The approach is applied to the prediction of the Mackey–Glass time series, and results are shown to outperform the results produced by other neural models. Gaussian noise was applied to the test data of the Mackey–Glass time series for finding out which of the presented methods in this paper shows better performance and tolerance to noise. [ABSTRACT FROM AUTHOR]

This paper proposes an optimum design method for two-dimensional heat conduction problem with heat transfer boundary condition based on the boundary element method (BEM) and the topology optimization method. The level set method is used to represent the structural boundaries and the boundary mesh is generated based on iso-surface of the level set function. A major novel aspect of this paper is that the governing equation is solved without ersatz material approach and approximated heat convection boundary condition by using the mesh generation. Additionally, the objective functional is defined also on the design boundaries. First, the topology optimization method and the level set method are briefly discussed. Using the level set based boundary expression, the topology optimization problem for the heat transfer problem with heat transfer boundary condition is formulated. Next, the topological derivative of the objective functional is derived. Finally, several numerical examples are provided to confirm the validity of the derived topological derivative and the proposed optimum design method. [ABSTRACT FROM AUTHOR]

It is well known that one of the basic advantages of evolutionary programming is in deciding the most suitable place for breeding offspring and finding the route toward the global minimum. To reach this goal, the algorithm needs to estimate the coordinates of the global minimum and then steer the new point toward it. In this paper, the estimation of the global minimum is calculated by weighted mean coordination of individuals (WMP), and then a road is mapped between the coordinates of the parents and the WMP. In the proposed method, fuzzy logic is used for deciding on the road and the best coordination to breed offspring. The proposed algorithm is tested on 65 well-known cost functions and is compared with five algorithms inside the EP family. In the next section of the paper, the algorithm is tested on the high-dimensional problem of modeling ozone layer data, which includes almost 26,000 unknown parameters. The results demonstrate the capability of the proposed method in having acceptable speed and accuracy. [ABSTRACT FROM AUTHOR]

Alternation was first proposed by Chandra et al. for obtaining a theoretical model of parallel computations. Compared with nondeterminism, alternation gives computing devices the power of universal choice in addition to existential choice. In this paper, we put forward a notion of fuzzy alternating Büchi automata over a distributive lattice L ( L -ABAs, for short). In our setting, a weight is the label of a leaf node of the run tree when executing a transition, which is helpful in complementing L -ABAs. Taking the dual operation on the transition function and negating final costs on states, we can get the complement of a given L -ABA. We point out that L -ABAs have the same expressive power as L -valued fuzzy nondeterministic Büchi automata ( L -NBAs). A construction presented here shows that languages accepted by L -valued fuzzy alternating co-Büchi automata ( L -ACAs) are also ω -regular languages. Moreover, closure properties of L -ABAs and decision problems for L -ABAs are also discussed. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs) that is inspired by the collective foraging behavior of real honey bee colonies. Due to its foraging model and its solution search equation, ABC generally performs well in exploration but badly in exploitation. To address this concerning issue and obtain a good balance between exploration and exploitation, in this paper, we mainly introduce into the ABC an adaptive method for the population size (AMPS). AMPS is inspired by the natural principle that the size of a population is affected by the availability of food resources. When food resources are abundant, a population tends to expand; otherwise, a decrease in the availability of food resources leads to a shrinkage in the population size. Specifically, when the algorithm performs well in exploration, AMPS will shrink the population to enhance exploitation by periodically removing some inferior solutions that have low success rates. In contrast, AMPS will enlarge the population to improve exploration by introducing some reserved solutions. Furthermore, to make AMPS perform better, we design a new solution search equation for employed bees and onlooker bees. Moreover, we also improve the probability model of the onlooker bees. By embedding our three proposed algorithmic components into ABC, we propose a novel ABC variant, called APABC. To demonstrate the performance of APABC, we compare APABC with some state-of-the-art ABC variants and some other non-ABC methods on 22 scalable benchmark functions and 30 CEC2014 test functions. The simulation results show that APABC is better than or at least competitive with the competitors in terms of its solution quality, robustness and convergence speed. [ABSTRACT FROM AUTHOR]

As a special case of information table, multi-scale decision table can usually be observed in real-life world. In such table, objects may take different values under the same attribute measured at different scales. Based on inclusion relation of subsets of attributes and coarse relation of scales of attributes, multi-layered granulations and stratified rough set approximations in multi-scale decision tables are shown from the perspective of granular computing. Compared with a special case studied by Wu and Leung, the multi-scale decision tables of diverse attributes with different numbers of levels of scales are studied in this paper. Furthermore, complement model and lattice model are proposed to analyze the optimal scale selection for multi-scale decision tables. Correspondingly, algorithms of the two models are designed and some experiments are performed to testify feasibilities of these proposed algorithms and to make comparisons of the models. [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 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]

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]

In some real-world decision processes, decision makers may prefer to provide their opinions using linguistic expressions instead of a single linguistic term. Particularly, they may hesitate between several linguistic terms. In this paper, we deal with the consensus issue in the hesitant linguistic group decision making (GDM) problem. Firstly, a novel distance-based consensus measure is proposed. Then, using this consensus measure we develop an optimization-based consensus model in the hesitant linguistic GDM, which minimizes the number of adjusted simple terms in the consensus building. Furthermore, a two-stage model is displayed to further optimize the solutions to the proposed consensus model, through which we obtain the unique optimal adjustment suggestion to support the consensus reaching process in the hesitant linguistic GDM. Finally, several desirable properties are proposed to justify the proposal, and two examples are used to demonstrate the validity of the models. [ABSTRACT FROM AUTHOR]

In certain real decision-making situations, decision makers often feel more comfortable providing their knowledge and preferences in linguistic terms. Unbalanced linguistic term sets may be used in decision problems with preference relations. However, the lack of consistency in decision-making with linguistic preference relations can lead to inconsistent conclusions. Based on the consistency measure of unbalanced linguistic preference relations, this paper proposes an optimization-based approach to improving the consistency level of unbalanced linguistic preference relations. This consistency-improving model preserves the utmost original knowledge and preferences in the process of improving consistency. Furthermore, it guarantees that the elements in the optimal adjusted unbalanced linguistic preference relation are all simple unbalanced linguistic terms. Finally, we propose a mixed 0–1 linear programming aimed to obtain the optimum solution to the proposed consistency improving model and to demonstrate its practicability. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization method inspired by a paradigm from nature is introduced. Chemical reactions are used as a paradigm to propose an algorithm that can be considered as a general purpose optimization technique. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a joint convex penalty for estimating the Gaussian inverse covariance matrix. A proximal gradient method is developed to solve the resulting optimization problem with more than one penalty constraints. The analysis shows that imposing a single constraint is not enough and the estimator can be improved by a trade-off between two convex penalties. The developed framework can be extended to solve wide arrays of constrained convex optimization problems. A simulation study is carried out to compare the performance of the proposed method to graphical lasso and the SPICE estimate of the inverse covariance matrix. [Copyright &y& Elsevier]