Search

In the hesitant fuzzy decision-making process, an important issue is to aggregate the hesitant fuzzy information and select the optimal alternatives. How to deal with other non-optimal alternatives? This question has not been clearly answered. To address this issue, this paper proposes the hesitant fuzzy envelopment analysis (HFEA) model based on the defined score per unit. Furthermore, we develop the deviation-oriented hesitant fuzzy envelopment analysis (DHFEA) model and the score-oriented hesitant fuzzy envelopment analysis (SHFEA) model in terms of score and deviation values. Then, their dual forms are derived so that the DHFEA and SHFEA models can be transformed into the linear programming. Based on the calculated results, the non-optimal alternatives can be improved accordingly. Moreover, with respect to the attributes’ differences, this paper constructs the hesitant fuzzy preference envelopment analysis (HFPEA) model by integrating the attributes’ preferences. Similarly, its dual form, linear transformation , and alternative improvement method are investigated respectively. Thus, based on the HFEA and HFPEA models, we can rank all the alternatives and improve the non-optimal ones according to the envelopment values and the obtained parameters. Meanwhile, the decision maker's subjective preferences for the attributes can be considered in the decision-making process. Finally, an illustrative example of bidding evaluation is provided to show the feasibility of the proposed decision-making approaches.

Finding graph measures with high discrimination power has been triggered by searching for so-called complete graph invariants. In a series of papers, we have already investigated highly discriminating measures to distinguish graphs (networks) based on their topology. In this paper, we propose an approach where the graph measures are based on the roots of random graph polynomials. The polynomial coefficients have been defined by utilizing information functionals which capture structural information of the underlying networks. Our numerical results obtained by employing exhaustively generated graphs reveal that the new approach outperforms earlier results in the literature.

DNA sequence reconstruction is a challenging research problem in the computational biology field. The evolution of the DNA is too complex to be characterized by a few parameters. Therefore, there is a need for a modeling approach for analyzing DNA patterns. In this paper, we proposed a novel framework for DNA pattern analysis. The proposed framework consists of two main stages. The first stage is for analyzing the DNA sequences evolution, whereas the other stage is for the reconstruction process. We utilized cellular automata (CA) rules for analyzing and predicting the DNA sequence. Then, a modified procedure for the reconstruction process is introduced, which is based on the Probabilistic Cellular Automata (PCA) integrated with Particle Swarm Optimization (PSO) algorithm. This integration makes the proposed framework more efficient and achieves optimum transition rules. Our innovated model leans on the hypothesis that mutations are probabilistic events. As a result, their evolution can be simulated as a PCA model. The main objective of this paper is to analyze various DNA sequences to predict the changes that occur in DNA during evolution (mutations). We used a similarity score as a fitness measure to detect symmetry relations, which is appropriate for numerous extremely long sequences. Results are given for the CpG-methylation-deamination processes, which are regions of DNA where a guanine nucleotide follows a cytosine nucleotide in the linear sequence of bases. The DNA evolution is handled as the evolved colored paradigms. Therefore, incorporating probabilistic components help to produce a tool capable of foretelling the likelihood of specific mutations. Besides, it shows their capabilities in dealing with complex relations.

In this paper we investigate an application of hybrid Monte Carlo Tree Search (MCTS) based algorithms to solving dynamic decision making problems. We employ UCT (the most popular MCTS approach) in combination with well-known Resource Constrained Project Scheduling Problem (RCPSP) and Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) solvers to devise strategies for a generic and highly dynamic version of RCPSP, which we call Risk-Aware Project Scheduling Problem (RAPSP). We compare these strategies’ performance with results of both pure MCTS approach and non-MCTS solvers for projects of varied characteristics. We reach a conclusion that proposed hybrid simulation-heuristic methods are promising approaches to dynamic decision making problems, RAPSP in particular. Consequently, we argue that more research effort should be directed to applications of MCTS algorithm outside the domain of game-playing, with which it is commonly associated. At the same time, to the best of our knowledge, this paper is the first attempt at defining generalized SRCPSP model encompassing arbitrary risks and risk response / mitigation strategies as an optimization problem and applying Computational Intelligence methods to build fully-automated decision making systems. We strongly believe it to be a research direction worth further investigation, combining project scheduling, risk management and metaheuristic optimization techniques into a well-defined platform allowing direct comparisons of different strategies.

The Shortest Path in networks is an important problem incombinatorial optimization and has many applications in areas like telecommunications and transportation. It is known that this problem is easy to solve in its classic deterministic version, but it is also known that it is an NP-Hard problem for several generalizations. The Shortest Path Problem consists in finding a simple path connecting a source node and a terminal node in an arc-weighted directed network. In some real-world situations the weights are not completely known and then this problem is transformed into an optimization one under uncertainty. It is assumed that an interval estimate is given for each arc length and no further information about the statistical distribution of the weights is known. Uncertainty has been modeled in different ways in optimization. Our aim in this paper is to study the Minmax Regret path with interval data problem by presenting a new exact branch and cut algorithm and, additionally, new heuristics. A set of difficult and large size instances are defined and computational experiments are conducted for the analysis of the different approaches designed to solve the problem. The main contribution of our paper is to provide an assessment of the performance of the proposed algorithms and an empirical evidence of the superiority of a simulated annealing approach based on a new neighborhood over the other heuristics proposed.

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.

In this paper, the problem of event-triggered consensus for linear continuous-time multi-agent systems is investigated. A new event-triggered consensus protocol based on a predictor is proposed to achieve consensus without continuous communication among agents. In the proposed consensus protocol, each agent only needs to monitor its states to determine its event-triggered instants. When an event is triggered, the agent will update its consensus protocol and sent its state information to its neighbors. In addition, the agent will also update its consensus protocol and the predictor when it receives the state information from its neighbors. A necessary and sufficient condition that the consensus problem can be solved is derived. Moreover, it is proved that Zeno behavior does not exist. Finally, a numerical example is given to illustrate that the protocol proposed in this paper can make the multi-agent systems achieve consensus through much fewer event-triggered times., 7 pages, 3 figures, 1 table, 29 references

Experts are often solicited to provide their opinions on systems unavailable inputs. In certain cases, we can have several opinions for each system input. In such situation, we ask ourselves what is the best way to combine these opinions? For each input, we can either combine them, before their propagation into the system model, or combine them after the propagation of each opinions combination separately. The purpose of this paper is to explore the differences between these two aggregation modes. For our reliability model, outcomes show that the location of the operating point in the probabilistic space and the divergence (gap) between expert opinions are the main factors explaining the difference between both aggregation modes. In this paper, we propose the Divergence Metric δ to measure the divergence between experts’ opinions and we suggest the use of the ‘Cumulative Distribution Averaging’ as an aggregation rule. This rule seems suitable for probabilistic and non-probabilistic opinions and it avoids limitations encountered with expert opinions expressed according to bounded distributions.

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper., Comment: 22 pages, 8 figures, 10 tables, journal

This paper aims to enhance the reliability and quality of service (QoS) of power smart grids by searching and applying reconfiguration oriented solutions. A novel definition of recovery performance is provided in terms of automatic recoverability and unavailability rates. The proposed approach is based on a hierarchical and distributed multi-agent system (MAS). It involves static and mobile agents , respectively, dedicated to search for new feasible reconfigurations and to identify the consumers which may play the role of feeders over smart grids . An automatic reconfiguration corresponds to the addition of a new pertinent line in a smart grid to allow further recoveries. This paper provides the implemented communication protocol for negotiating (CPN) the resources allocation which is performed by the deployed agents. The management and assignment of the energy resources are modeled as a combinatorial optimization multiple-knapsack problem (MKP) to reach a better configuration with a higher possible recovery performance rate. To decrease the cost of automatic actions, which increases the smart grid performance and reduces the costs of the human interventions, a short list of failures is identified based on proposed relations of dominance and equivalence between observed failures. Furthermore, this work investigates the effectiveness of the designed and implemented software tool for off-line assistance-design, simulation and modeling of reconfigurable smart grids. The efficiency of the proposed strategy is validated through an experimental study performed by the developed simulator.

During the past decade, graph-based learning methods have proved to be an effective tool to make full use of both labeled and unlabeled data samples to improve learning performance. These methods try to discover the intrinsic structures and discriminative information embedded in the data, by building one or more graphs to model the relationship among the data samples. Consequently, how to build an effective graph is the core problem. In this paper we introduce a novel graph-based classification method, called Supervised clustering-based Regularized Least Squares Classification (SuperRLSC), in which local and global graphs of the data are built by supervised clustering. The motivation is that supervised clustering may discover more actual data structures compared to unsupervised clustering. In our algorithm, we firstly employ supervised k-means to partition the whole training dataset into several meaningful clusters in order to discover the intrinsic and discriminative structures. We then use the discovered structures to build local and global graphs of the data. The local graph reveals local geometric and discriminative structures, while the global graph reveals global discriminative information. Finally a hybrid local/global graph-based regularization term is embedded into supervised classification (i.e., RLSC in this paper). To validate the effectiveness of our algorithm, a series of experiments are performed on several UCI benchmark datasets. The results show that our algorithm can achieve better or at least comparable performance to the other graph-based algorithms and the traditional state-of-the-art supervised classification methods.

In this paper to adapt radial moment based watermarking for reversible cases, we have developed non-unit mapped radial moments. They have been used to extract the image invariant features to produce robust geometric invariant watermarking and reversible data hiding. This aim has been achieved through using two other newly introduced methods called reversible interval phase modulation and diagonal rotation estimation. In this paper, at first a mathematical procedure to compute these newly modified radial moments has been proposed. Then a watermarking platform has been suggested which can be used for either robust or reversible data hiding fields. The proposed robust watermarking provides robustness against signal processing and both local and global geometric attacks while at the same time the achieved robust watermarked image can remain integer as the original one. Also, the proposed radial moment computation procedure has been used to produce reversible data hiding. Reversible interval phase modulation and diagonal rotation estimation methods have been proposed to avoid any type of normalization-like degradation in the embedding and extraction. Various aspects of the proposed watermarking scheme have been thoroughly examined through several experiments and the results are highly promising.

In this paper, we use the cosine function to express the data energy fitting of a traditional active contours model and propose a model based on sectional image recovery local cosine-fitting energy active contours, which is used to segment medical and synthetic images . The algorithm is a single level image segmentation method. It can process synthetic images with intensity inhomogeneity . Moreover, our model for the images with noise and the fuzzy ones is more efficient and robust, and the computational speed was similar or faster, compared with Convex Variant of the Mumford–Shah Model and Thresholding (CVMST) model, a local binary fitting (LBF) model and L0 Regularized Mumford–Shah (L0MS) model. In addition, we describe the model in a discrete form, which is more convenient to add a regular term to control the segmentation. Therefore the massive calculation is reduced by re-initializing the level set curve. At the end of the paper, the modified algorithm has been utilized to segment medical images and three-dimensional visualization results are obtained. The experimental results indicate that the segmentation results are accurate and efficient when applied to different kinds of images.

This paper investigates the problem of distributed consensus control for multi-agent systems under denial-of-service (DoS) attacks. Different from the existing results where DoS attacks on all the channels are same, in this paper, the adversaries compromise each channel independently. The objective is to design distributed controllers such that the consensus is still achieved in the presence of DoS attacks. Both state-feedback and observer-based controllers are considered. First, the decay rates under different attack modes are obtained by solving a class of linear matrix inequalities. Second, sufficient conditions on the duration of the DoS attacks, under which the consensus is still achieved, are proposed. The difficulty that there is no one-to-one match between the obtained decay rates and DoS duration limitations, is overcome by introducing the equivalent decay rates corresponding to channels. Moreover, the computational complexity is reduced greatly by introducing a novel scaling method. Finally, two examples are presented to illustrate the effectiveness of the proposed approaches.

This paper discusses transmission power scheduling and control co-design in wireless sensor networks (WSNs) under communication energy constraints, in which the dynamics of all mobile sensor nodes (MSNs) are assumed to be nonlinear. A most regular code sequence (MRCS) is introduced as the transmission power scheduling policy to automatically allocate the transmission power levels to MSNs thereby adhering to the given communication energy budget. The effects of the different transmission power levels on data transmission is explicitly characterized in this paper. By using a stochastic system analysis approach and average dwell time technique, a framework for the transmission power scheduling and control co-design is established. The control co-design algorithm is proposed to simultaneously determine the gain of the sampled-data feedback controller and the parameters of the generated MRCS. A numerical simulation is conducted to verify the effectiveness of the proposed algorithm.

Pythagorean fuzzy sets (PFSs) as a new generalization of intuitionistic fuzzy sets (IFSs) can effectively handle uncertain information more flexibly in the process of decision making. As a natural extension of three-way decisions with decision-theoretic rough sets (DTRSs), this paper proposes a new model of three-way decisions and develops the corresponding decision-making procedure based on Pythagorean fuzzy information systems. With respect to the results reported in most of the existing papers, we consider a general situation that the information system does not have the class label. In this case, we can encounter two challenges and need to reinterpret the loss function and the conditional probability . Considering the properties of PFSs, we firstly introduce the Pythagorean fuzzy number (PFN) into DTRSs, which can provide a new interpretation for the loss function. Then, we construct a new model of Pythagorean fuzzy decision-theoretic rough sets (PFDTRSs) based on the Bayesian decision procedure. With respect to the conditional probability, we effectively utilize the technique for order preference by similarity to ideal solution (TOPSIS) method to estimate it. Furthermore, we design a decision-making procedure of three-way decisions-based ideal solutions in the Pythagorean fuzzy information system. Our proposed method not only takes the decision risk into consideration, but also tells us how to choose the action for each project and gives its corresponding semantic explanation, which can replenish the decision results of TOPSIS . Finally, we expound the application of three-way decisions by an example of the research and development (R&D) project selection and validate our method via the comparison analysis.

Encryption is one of the most straightforward methods for ensuring the confidentiality of outsourced data on the cloud. However, encryption makes queries more difficult to perform. In recent years, new encryption schemes for facilitating queries have been proposed. However, for these schemes, some cannot support the scenario of multiple users, some are inefficient, and some are not sufficiently flexible (users must always ask the data owner for the tokens that are used for searching ciphertexts on the cloud). In this paper, we propose a scheme that supports efficient and flexible range search over ciphertexts in the scenario of multiple users. In our scheme, we construct an Encrypted Interval Tree (EIT) as the index for ciphertexts. The data owner outsources the EIT and ciphertexts to the cloud, and later distributes secret parameters (search keys, navigation paths and signatures) to users. By utilizing these secret parameters, users can generate tokens for the queried ranges without communication with the data owner and subsequently use the tokens to perform range search over ciphertexts on the cloud. Moreover, the signature technique is adopted in our scheme. Thus, the cloud can authenticate the identifiers of users and determine the legality of tokens. In this paper, we implement our scheme and conduct extensive experiments. The experimental results demonstrate the efficiency of our scheme. Finally, we analyze the security of our scheme.

In this paper, we propose a novel multimodal user identification and verification scheme combining two inter-linked biometric traits, i.e., signature and brain signals (Electroencephalography (EEG)). Moulding handwritten strokes for signature stimulates EEG signals in the brain. The response of the brain signals during signing is unique for each user and this brain signal-signature relation is utilized in the proposed scheme. To the best of our knowledge, there exist no authentication approach combining these two inter-linked biometric traits. The proposed multimodal scheme uses Hidden Markov Model (HMM) based sequential classifier to model features extracted from signatures and EEG signals individually. Pyramid Histogram of Orientation Gradients (PHOG) features are extracted from the signature-images and next PHOG features are used to build user specific signature-HMM models. Similarly, user wise EEG-HMM models are built with Daubechies-4 (DB4) wavelet analysis. Finally, a score combining classification scores of signature-HMM and EEG-HMM models is used to perform user identification and verification. To evaluate the effectiveness of the proposed scheme, we have developed a dataset collecting these two traits simultaneously using the Emotiv Epoc+ device and pen-paper for 70 individual subjects. Thereafter, user’s identification is performed with individuals’ signature and EEG signals as well as their combined traits. The identification accuracy of the proposed multimodal approach has been achieved upto 98.24%. The effectiveness of the verification scheme is validated by the Receiver Operating Characteristic (ROC) curves.

A novel sliding mode fuzzy control problem of nonlinear stochastic systems is discussed subject to pole assignment and multiple performance requirements via Takagi–Sugeno fuzzy model approach in this paper. The requirements considered in this paper include output variance constraint, individual state variance constraint, while all poles are assigned into a specified disk. First, a reaching controller is designed to achieve a proposed integral sliding surface. Moreover, a fuzzy controller based on parallel distributed compensation is designed via full state feedback scheme to guarantee pole assignment and multiple performance requirements. Based on Lyapunov theory, some sufficient conditions are derived into linear matrix inequality form that can be directly solved by the convex optimization algorithm. Through using the proposed design method, a sliding mode fuzzy controller can be established to guarantee multiple performance constraints for nonlinear stochastic systems. Finally, a numerical example for the control of nonlinear synchronous generator is employed to demonstrate the effectiveness and application of the proposed design method.

This paper introduces βHCWT, a hybrid of the β-hill climbing metaheuristic algorithm and wavelet transform (WT), as a new method for denoising electrocardiogram (ECG) signals. ECG signals are non-stationary signals that provide a graphical measure of electrical activities in human heart muscles. However, given their non-stationarity, these signals frequently encounter noise and a low signal-to-noise ratio (SNR). The selection of wavelet parameters is a challenging task that is usually performed based on empirical evidence or experience. Therefore, in this paper, β-hill climbing is applied to find the optimal wavelet parameters that can obtain the minimum mean square error (MSE) between the original and denoised ECG signals. The proposed method was tested on a standard ECG dataset from MIT-BIH while its performance was evaluated by using percentage root mean square difference (PRD) and SNR as criteria. Meanwhile, the effect of β-hill climbing on the performance of WT was tested by comparing the proposed method with WT. The proposed method was then compared with the genetic algorithm in consideration of the performance of the WT parameters and adaptive thresholding methods. The proposed method demonstrated an outstanding performance in removing noise from non-stationary signals, and the quality of the output signal was deemed favorable for medical diagnosis.

This paper presents a general framework to demonstrate the prominent role of diversity in the effectiveness of collective performance. There appears to be ample evidence that diversity is one of the essential criteria of which a collective to be intelligent. Intuitively, a collective involving diverse individuals may add new information, new perspectives, and so forth on the problem that needs to be solved. Moreover, the diversity of individual solutions to the given problem has been proven helpful in eliminating the phenomenon of correlated errors. The objective of the paper is to investigate the influence of the latter kind of diversity on the collective performance by taking into account the collective cardinality. Our findings qualify the positive impact of diversity on collective performance. Particularly, collectives with higher diversity levels will lead to better collective performances. Subsequently, expanding the collective cardinality that causes an increase in its diversity will also be positively associated with the collective performance. With some restrictions, the hypothesis “the more diverse the collective, the higher the collective performance” is formally proved. Furthermore, the conditions under which increasing the cardinality of a collective will cause its diversity to be increased (or decreased) are worked out.

This paper studies a class of distributed optimization algorithms by a set of agents, where each agent only has access to its own local convex objective function, and the goal of the agents is to jointly minimize the sum of all the local functions. The communications among agents are described by a sequence of time-varying directed graphs which are assumed to be uniformly strongly connected. A column stochastic mixing matrices is employed in the algorithm which exactly steers all the agents to asymptotically converge to a global and consensual optimal solution even under the assumption that the step-sizes are uncoordinated. Two fairly standard conditions for achieving the geometrical convergence rate are established under the assumption that the objective functions are strong convexity and have Lipschitz continuous gradient. The theoretical analysis shows that the distributed algorithm is capable of driving the whole network to geometrically converge to an optimal solution of the convex optimization problem as long as the uncoordinated step-sizes do not exceed some upper bound. We also give an explicit analysis for the convergence rate of our algorithm through a different approach. Finally, simulation results illustrate the feasibility of the proposed algorithm and the effectiveness of the theoretical analysis throughout this paper.

Social networking services allow users to adopt and spread information via diffusion actions, e.g., share, retweet, and reply. Real applications such as viral marketing and trending topic detection rely on information diffusion. Given past items with diffusion records on a social network, this paper aims at forecasting who will participate in the diffusion of a new item c (we use hashtags in the paper) with its k earliest adopters, without using content and profile information, i.e., finding which users will adopt c in the future. We define the Diffusion Participation Forecasting (DPF) problem, which is challenging since all users except for early adopters can be the candidates, comparing to existing studies that predict which one-layer followers will adopt a new hashtag given past diffusion observations with content and profile info. To solve the DFP problem, we propose an Adoption-based Participation Ranking (APR) model, which aims to rank the actual participants in reality at higher positions. The first is to estimate the adoption probability of a new hashtag for each user while the second is a random walk-based model that incorporates nodes with higher adoption probability values and early adopters to generate the forecasted participants. Experiments conducted on Twitter exhibit that our model can significantly outperform several competing methods in terms of Precision and Recall. Moreover, we demonstrate that an accurate DPF can be applied for effective targeted marketing using influence maximization and boosting the accuracy of popularity prediction in social media.

Keyword Spotting (KWS) systems can be divided into two main groups: Hidden Markov Model (HMM)-based and Discriminative KWS (DKWS) systems. In this paper, we propose an approach to improve a DKWS system using advantages of HMM-based systems. The proposed DKWS system contains feature extraction and classification (that includes a classifier and a search algorithm) parts. The focus of this paper is on the feature extraction part and the search algorithm. At first, we propose a method for using the advantages of a triphone-based HMM system and improving the monophone-based feature extraction, (proposed in our previous works), to triphone-based one. Then, we propose an N-best search algorithm instead of one-best algorithm. The results on TIMIT database indicate that the true detection rate of the triphone-based Evolutionary DKWS (EDKWS) system with N-best search (Tph-EDKWS-N-Best), in false alarm rate per keyword per hour greater than two, is 4.6% higher than that of the monophone-based EDKWS system with one-best search (Mph-EDKWS-1-Best). This improvement costs about 0.4 unit degradation in Real Time Factor (a common metric of measuring the speed of an automatic speech recognition system). Additionally, Figure of Merit (average true detection rate for different false alarm per keyword per hour from 1 to 10) of the Tph-EDKWS-N-Best system is noticeably higher than that of HMM-based KWS systems. However, the computational complexity of the Tph-EDKWS-N-Best system is considerably higher than that of the HMM-based KWS systems.

Identifying faulty program entities has been recognized as one of the most expensive, tedious and time-consuming processes in software debugging activity. Fault localization techniques are designed to assist developers in locating faults by giving a ranking of the probability that program entities incur failures. Mutation-based fault localization (MBFL) is a recently proposed fault localization approach via mutation analysis, which uses the location of mutants to identify the faulty statements. With improved effectiveness, the MBFL also brings huge execution cost. To reduce the execution cost of MBFL technique, this paper proposes a dynamic mutation execution strategy (DMES) to prioritize the execution on both mutants and test cases. As fewer mutants and test cases are executed with DMES, the whole process will become faster and the cost will be decreased. At the same time, it is proved that the fault localization accuracy of MBFL with DMES is the same as that of the original MBFL. Furthermore, this paper discusses the lowest mutation execution cost of MBFL in theory and gives a quick solution to compute the theoretical minimal mutation execution cost of MBFL in the case of keeping fault localization accuracy non-decreasing. The empirical studies show that MBFL with the dynamic strategy proposed in this paper can reduce mutant-test execution cost by 32.4–87% and is close to the theoretical optimal cost. Furthermore, the additional run time required by utilizing our strategy is minimum and can be ignored.

The performance of differential evolution (DE) has been significantly influenced by trial vector generation strategies and control parameters. Various powerful trial vector generation strategies with adaptive parameter adjustment methods such that the population generation is guided by the elites have been proposed. This paper aims to strengthen the performance of DE by compositing these powerful trial vector generation strategies, making it possible to obtain the guidance of each individual from multiple elites concurrently and independently. In this manner, the deleterious behavior in which an individual is misguided by various local optimal solutions into unpromising areas could be restrained to a certain extent. An adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism (abbreviated as AMECoDEs) has been proposed in this paper. This algorithm concurrently employs two elites-guided trial vector generation strategies for each individual to generate two candidate solutions accordingly, and the best one is adopted to participate in the selection. Moreover, a novel shift mechanism is established to handle stagnation and premature convergence issues. AMECoDEs has been tested on the CEC2014 benchmark functions. Experimental results show that AMECoDEs outperforms various classic state-of-the-art DE variants and is better than or at least comparable to various recently proposed DE methods.

This paper addresses the control problem of MIMO stochastic nonlinear systems with unknown high-frequency gains. In the existing works, the prior knowledge of the high-frequency gain or its control direction is assumed known for the control design. This paper removes such assumptions, and proposes an adaptive neural network algorithm that allows the high-frequency gains to be time-varying and their control directions to be unknown. Nussbaum gain based approach and adaptive neural network mechanism are brought together such that all the signals in the closed-loop system are ensured bounded. A simulation study is carried out to confirm the validity of the proposed algorithm.

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm.

This study provides a comprehensive overview of the publications in Information Sciences (INS) from 1968 to 2016 inclusive, which encompasses the history of this journal from its inception. 7721 articles containing 153,606 references, which are the primary data source were downloaded from the Web of Science. It studies the most prolific authors, most cited authors, most representative articles, top influential institutions and the nationalities of the authors whose papers were published in the journal. The key contributors and INS articles that have made profound impact are highlighted on a basis of bibliometric and customized text mining techniques. CiteSpace, a data visualization software, was used to make the comprehensive analysis of the 153,606 citations and construct the co-citation network maps, which can illustrate salient patterns and emerging trends. This paper not only provides the important reference to future studies by exploring the structures and trends of INS publications, which have evolved over time, but also offers a demonstration of an effective analytical method for evaluating journal citation and co-citation data in the future.

This paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a well-distributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions.

The traditional redundancy allocation problem (RAP) aims to determine a system structure that strikes an appropriate balance between series and parallel components and that maximizes reliability by using redundant components in parallel, thus satisfying resource consumption constraints. The RAP is a well-known NP-hard problem and has been an active area of research for the past four decades. By extending the seriesparallel structure to a more general network structure, this paper proposes a novel RAP called the generalized RAP (GRAP). To reduce the computational burden in solving this intractable GRAP, a new algorithm, called the hybrid swarm optimization (HSO), is proposed. The HSO combines concepts from simplified swarm optimization (SSO), particle swarm optimization (PSO), simulated annealing, and network reliability methods. The paper presents designed experiments and computational studies that demonstrate the potential of using the proposed HSO for the GRAP instead of using the genetic algorithm, PSO, or SSO.

Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

Support vector machine (SVM) is one of the most successful classifiers for remote sensing image classification. However, the performance of SVM is mainly dependent on its parameters; in addition, for remote sensing images with high-dimensional features, feature redundancy will have a major influence on the classification efficiency and accuracy. Feature selection and parameter optimization are two important factors for improving the performance of SVM and are traditionally solved separately. In fact, these two issues are affected by each other, so to obtain the better classification performance, selection of the optimal feature subset and tuning of SVM parameters should be considered simultaneously, as they both belong to the combinatorial optimization problem and could be handled with evolutionary algorithms or swarm intelligence algorithms. In this paper, a remote sensing image classification technique based on the optimal SVM is proposed, in which the parameters of SVM and feature selection are handled integrally by a modified coded ant colony optimization algorithm combined with genetic algorithm. The results are compared with other evolutionary algorithms and swarm intelligence algorithms, such as genetic algorithm (GA), binary-coded particle swarm optimization (BPSO) algorithm, binary-coded ant colony optimization (BACO) algorithm, binary-coded differential evolution (BDE) algorithm, and binary-coded cuckoo search (BCS) algorithm. It is demonstrated that the proposed method is robust, adaptive and exhibits the better performance than the other methods involved in the paper in terms of fitness values, so could be suitable for some practical applications.

A novel concept extended salient region is proposed in this paper. It is defined as the combination of the salient region and its surrounding ones.A graph model with visual content nodes is proposed to represent the input image. We extract the visual content with the well-known Bag of Words model based on Gabor, SIFT and HSVH features.A novel matching algorithm between two images is designed. We define a new similarity measure by combining the similarities of the salient region and the surrounding regions with weights.An improved measure named as mean label average precision is proposed. It is used to evaluate the accuracy of image retrieval. The salient region is the most important part of an image. The salient portion in images also attracts the most attention when people search for images in large-scale datasets. However, to improve image retrieval accuracy, considering only the most salient object in an image is insufficient because the background also influences the accuracy of image retrieval. To address this issue, this paper proposes a novel concept called the extended salient region (ESR). First, the salient region of an input image is detected using a Region Contrast (RC) algorithm. Then, a polar coordinate system is constructed; the centroid of the salient region is set as the pole. Next, the regions surrounding the salient region are determined by the neighboring regions, moving in a counterclockwise direction. The resulting combination of the salient region and its surrounding regions is defined as the ESR. We extract the visual content from the ESR using the well-known Bag of Words (BoW) model based on Gabor, SIFT and HSVH features and propose a graph model of the visual content nodes to represent the input image. Then, we design a novel algorithm to perform matching between two images. We also define a new similarity measure by combining the similarities of the salient region and the surrounding regions using weights. Finally, to better evaluate the image retrieval accuracy, an improved measure called the mean label average precision (MLAP) is proposed. The results of experiments on three benchmark datasets (Corel, TU Darmstadt, and Caltech 101) demonstrate that our proposed ESR model and region-matching algorithm are highly effective at image retrieval, and can achieve more accurate query results than current state-of-the-art methods.

Deep Neural Networks for image/video classification have obtained much success in various computer vision applications. Existing deep learning algorithms are widely used on RGB images or video data. Meanwhile, with the development of low-cost RGB-D sensors (such as Microsoft Kinect and Xtion Pro-Live), high-quality RGB-D data can be easily acquired and used to enhance computer vision algorithms [14]. It would be interesting to investigate how deep learning can be employed for extracting and fusing features from RGB-D data. In this paper, after briefly reviewing the basic concepts of RGB-D information and four prevalent deep learning models (i.e., Deep Belief Networks (DBNs), Stacked Denoising Auto-Encoders (SDAE), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) Neural Networks), we conduct extensive experiments on five popular RGB-D datasets including three image datasets and two video datasets. We then present a detailed analysis about the comparison between the learned feature representations from the four deep learning models. In addition, a few suggestions on how to adjust hyper-parameters for learning deep neural networks are made in this paper. According to the extensive experimental results, we believe that this evaluation will provide insights and a deeper understanding of different deep learning algorithms for RGB-D feature extraction and fusion.

Billions of resources, such as cars, clothes, household appliances and even food are being connected to the Internet forming the Internet of Things (IoT). Subsets of these resources can work together to create new self-regulating IoT applications such as smart health, smart communities and smart homes. However, several challenging issues need to be addressed before this vision of applications based on IoT concepts becomes a reality. Because many IoT applications will be distributed over a large number of interacting devices, centralized control will not be possible and so open problems will need to be solved that relate to building locally operating self-organizing and self-adaptive systems. As an initial step in creating IoT applications with these features, this paper presents a Framework for IoT (FIoT). The approach is based on Multi-Agent Systems (MAS) and Machine Learning Techniques, such as neural networks and evolutionary algorithms. To illustrate the use of FIoT, the paper contains two different IoT applications: (i) Quantified Things and (ii) Smart traffic control. We show how flexible points of our framework are instantiated to generate these IoT application.

We confirm that agent-ants can learn only via the pheromone trail.It is better to use both the heuristic function and the pheromone trail.The ACDT alg. makes it possible to optimize the construction of decision trees based on a heuristic.The pheromone map clearly present a data set as the solution space and pheromone trail values.Analysis of pheromone maps shows that the ACDT alg. searches different subspaces of the solution space. This paper considers the topic of cooperation between agent ants in an Ant Colony Optimization (ACO) algorithm that is used to construct decision trees (Ant Colony Decision Tree or ACDT). To follow a suitable methodology, the paper presents a formal definition of the ACDT algorithm with a focus on the influence that Ant Colony Optimization algorithms have on the obtained results. The aim of this paper is to provide the rationale for using swarm intelligence (i.e., ACO) in the process of constructing decision trees. Many experiments were conducted to provide a solid justification. These experiments tested cooperation between agent ants in ant colony algorithms with different ACO performance scenarios: the application of only a pheromone trail, the application of only a heuristic function, the application of both components, and the application of neither component. Additionally, different values of the pheromone trail were tested at various stages of the algorithm's operation and pheromone representations were presented. The experiments were conducted on 30 publicly available data sets; all observations were preceded by statistical tests. This paper confirms that it is reasonable to use the pheromone trail and balanced heuristics. Moreover, we found that, for the ACDT algorithm, good results can also be obtained without heuristics.

In recent years, there has been great progress in the wider use of three-dimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS) studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called "cyclopean saliency", which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judgment than the state-of-art SIQA metrics. 3D saliency performance is also compared with "cyclopean saliency" in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality.

In this paper we propose a design for the kNN rule classifier based on GPU devices.This design overcomes the dependency between dataset size and GPU memory required.An efficient CPU-GPU communication scheme is designed.The carefully designed kernels for each step of the computation improve the run-time performance.An efficient selection algorithm replaces the sorting algorithm traditionally used to find the neighborhood. The k nearest neighbor (kNN) rule is one of the most used techniques in data mining and pattern recognition due to its simplicity and low identification error. However, the computational effort it requires is directly related to the dataset sizes, hence delivering a poor performance on large datasets. The use of graphics processing units (GPU) has improved the run-time performance of the kNN rule but the computational requirements of current approaches limit this performance as the dataset size increases.In this paper, we propose a new scalable and memory efficient design for a GPU-based kNN rule, called GPU-SME-kNN, that breaks the dependency between dataset size and memory footprint while delivering high performance. An experimental study of GPU-SME-kNN is presented showing a high performance, even in cases that other methods cannot address, while the computational requirements are suitable for most commercial GPU devices. Our design has also been applied to kNN-based lazy learning algorithms reducing run-times in a significant way.

This paper addresses a multi-resource-constrained flexible job shop scheduling problem with makespan criterion.A dynamic shuffled multi-swarm micro-migrating birds optimizer (MBO) is proposed.Advanced technologies including micro-MBO, multi-swarm parallel search, random shuffle process, diversity controlling strategy, and adaptive search operator are introduced.The proposed algorithm outperforms the existing state-of-art algorithms in terms of solution quality and computational efforts.9 out of 10 best known solutions for the benchmarks in the literature are improved. Scheduling problems with resource constraints have been a new research trend in recent years. This paper addresses a multi-resource-constrained flexible job shop scheduling problem that is very common in semiconductor manufacturing, precision engineering, and many other modern industries. To address this important problem, a novel algorithm called the shuffled multi-swarm micro-migrating birds optimization (SM2-MBO) algorithm is presented with a two-vector representation. The SM2-MBO forms a number of micro-swarms, each of which performs its own MBO independently. A random shuffle process applied to the entire population is invoked periodically to propagate the good information that is found in some of the micro-swarms. A diverse controlling strategy based on the aging phenomenon of life is proposed to diversify the population. An adaptive search operator based on a problem-specific crossover and a two-vector crossover helps to balance exploitation and exploration. Numerical experiments and comparisons are conducted against the best performing algorithms reported in the literature for the considered problem. The results demonstrate that the proposed SM2-MBO performs significantly better than the existing algorithms in solving the multi-resource-constrained flexible job shop scheduling problem with the makespan criterion. Furthermore, the proposed SM2-MBO can improve 9 out of 10 best known solutions for the benchmark instances in the literature.

A closed-form local adaptive contrast operation is proposed.The motion consistency characteristic is explored for the first time.Spatio-temporal saliency is specifically modeled for dim moving target detection.The proposed approach can outperform existing approaches remarkably. Dim moving target detection from infrared image sequences, which lags behind the visual perception ability of humans, has attracted considerable interest from researchers due to its crucial role in airborne surveillance systems. This paper proposes a novel spatio-temporal saliency model to cope with the infrared dim moving target detection problem. Based on a closed-form solution derived from regularized feature reconstruction, a local adaptive contrast operation is proposed, whereby the spatial saliency map and the temporal saliency map can be calculated on the spatial domain and the temporal domain. In order to depict the motion consistency characteristic of the moving target, this paper also proposes a transmission operation to generate the trajectory prediction map. The fused result of the spatial saliency map, the temporal saliency map, and the trajectory prediction map is called the "spatio-temporal saliency map" in this paper, from which the target of interest can be easily segmented. A diverse test dataset comprised of three infrared image sequences under different backgrounds was collected to evaluate the proposed model; and extensive experiments confirmed that the proposed spatio-temporal saliency model can achieve much better detection performance than the state-of-the-art approaches.