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

• NACEEMD method is proposed to suppress the mode-mixing problem of EEMD. • A new screening criterion is proposed to screen out the effective IMFs. • EPAD algorithm is developed to implement the peak statistics-based thresholding. • Explored and expanded the application potential of the proposed denoising algorithm. This paper proposes an adaptive denoising methodology for noisy signals that employs a novel adaptive complementary ensemble empirical mode decomposition (NACEEMD) and a peak statistics (PS)-based thresholding technique. The key idea in this paper is the peak statistics (PS)-based thresholding technique,which breaks the traditional strategy with respect to selecting more accurate and more adaptive thresholds. The NACEEMD algorithm is proposed to decompose the noisy signal into a series of intrinsic mode functions (IMFs). At the same time, NACEEMD is also used to verify the applicability of the PS-based thresholding technique in different decomposition algorithms. The PS-based threshold is used to remove the noise inherent in noise-dominant IMFs, and the denoised signal is reconstructed by combining the denoised noise-dominant IMFs and the signal-dominant IMFs. This paper uses a various of simulated signals in various noisy environments for experiments, the experimental results indicate that the proposed algorithm outperforms traditional threshold denoising methodologies in terms of signal-to-noise ratio, root mean square error, and percent root distortion. Moreover, through real ECG signal and multi-sensor data fusion experiments, the application of the proposed algorithm in the field of engineering is explored and expanded. [ABSTRACT FROM AUTHOR]

This paper mainly proposes a new method for clustering linear ordinal ranking (LOR) information by agglomerative hierarchical clustering (AHC) algorithm. Considering that the cores of the AHC algorithm for LOR clustering are the difference measure among different LORs and the aggregation of the individual LORs, we firstly systematically analyze the existing studies for LOR distance measure, based on which we extend the method to depict LORs. Subsequently, the corresponding new distance measure is proposed starting from the perspective of utilizing the rankings' position information and relationship information together. In addition, we simplify the dominating index and dominated index-based aggregation method for LORs fusion. Further, we present a numerical case on online financial product recommendation to illustrate the usage of the algorithm and also try to provide a feasible way for online financial product recommendation. Then, we make some discussions on the proposed distance measure and the aggregation method under the framework of the AHC algorithm to show the features of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

For a multi-view learning task, it is crucial to assign appropriate weights to each view in order to learn complementary and consistent information across different views. In the field of multi-view clustering, most existing methods have been able to handle the weights of different views. However, these algorithms face the problem of unacceptable time complexity when dealing with large-scale datasets, and the learned similarity matrix fails to satisfy the graph regularization. In this paper, we propose an auto-weight learning method called multi-view clustering with graph regularized optimal transport. First, an anchor-based method is employed to overcome the problem of heavy time complexity when processing large-scale datasets, and it is able to automatically learn an appropriate weight for each view. Second, by introducing optimal transport we learn a regularized doubly-stochastic similarity matrix applicable to multi-view clustering tasks. Third, the optimal regularized anchor graph can be classified into specific clusters by adding a rank constraint. Finally, an effective optimization method is designed to optimize the formulated problem. Comprehensive experiments on multiple real-world datasets demonstrate that the proposed algorithm achieves superior performance to other state-of-the-arts algorithms. [ABSTRACT FROM AUTHOR]

Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance. [ABSTRACT FROM AUTHOR]

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

Tree-based ensemble algorithms (TEAs) have had a transformative impact in various fields. However, when they are applied to real-time critical problems such as medical analysis, existing TEAs fall short of two-fold issues. The first is the trade-off among model interpretability, accuracy, and explainability. Second, traditional TEAs perform dramatically worse when the number of contributing features to model accuracy is small in comparison to the total number of features. We address the aforementioned issues in this paper and propose a novel "eXplainable Reasonably Randomised Forest" (XRRF) algorithm. The XRRF consists of four conclusive steps: learning performance, intrinsic interpretability, model accuracy, and explainability. The proposed forest algorithm is evaluated on three real-world problems (medical analysis, business analysis, and employee churn), a hybrid artificial dataset, and twenty multidisciplinary benchmark problems with varying characteristics. The experimental results demonstrate that the XRRF outperforms the six mainstream and two cutting-edge black-box and white-box algorithms, respectively. [ABSTRACT FROM AUTHOR]

The combined heat and power economic dispatch (CHPED) problem is a complicated non-convex optimization problem with several constraints that has been a challenging and important task in the power system. Great efforts have been made to solve the small-scale CHPED problem, and there is considerable scope for research in the large-scale CHPED problem with lots of local optima. This paper presents a neighborhood-based differential evolution algorithm with direction induced strategy (NDIDE) to solve the large-scale CHPED problem. In NDIDE, a novel mutation strategy, named neighborhood nonelite direction induced strategy based on DE/rand/1 (DE/ldrand/1), is proposed to explore some untrodden areas of a search space using the direction information of pointing to inferior solutions within a small neighborhood. Such a strategy helps to strengthen the exploitation of some selected neighborhoods. Consequently, a good balance between exploration and exploitation abilities could be achieved. NDIDE is implemented on the CHPED systems with 7, 84, 96 and 192 units. The dispatch schedules obtained from NDIDE are feasible in three large CHPED systems and show the advantages of NDIDE in terms of cost savings and the robustness over the results obtained by three state-of-the-art differential evolution algorithms as well as recent works in this field. [ABSTRACT FROM AUTHOR]

The resilience of a query q (RES (q)) measures the minimum number of source tuples responsible for a specific query answer. If q is an IQ query, whose at most one attribute from a table has inequality relationships with attributions from other tables, the state-of-the-art work had shown that RES (q) is in PTIME. However, the state-of-the-art work is inefficient for computing the resilience of IQ queries. Moreover, the complexity of resilience for self-join free (sj-free) CQs with other kinds of inequalities has not been explored. Therefore, we provide a dichotomy for the complexity of resilience for sj-free CQs with inequalities q in this paper. We first introduce query condition graph, which includes a special query condition graph called query condition triad, and triangle vertex cover problem. Then we reduce the vertex cover problem to the triangle vertex cover problem. According to query condition graphs, our dichotomy is as follows: If q is a qc-triad query, which is not an IQ query but contains a query condition triad, then RES (q) is NP-complete. If q is a qc-linear query, which neither is an IQ query nor contains a query condition triad, then RES (q) is in PTIME. Finally, we introduce an algorithm called Rlin to compute the resilience of qc-linear queries, and an efficient algorithm named RIQ to efficiently compute the resilience of IQ queries. The experiments show that our RIQ has much better performance than other algorithms for computing the resilience of IQ queries. [ABSTRACT FROM AUTHOR]

In this paper, we will address the problem of how to compress sequences defined in partially commutative alphabets. The use of partial order between symbols may result in far more efficient compression schemes than the most commonly used compression techniques, which use total order. We suggest a new algorithm for compressing sequences defined in alphabets with a partial order. This new algorithm is named the Lempel–Ziv with Partial Order (POLZ) and stands for a Lempel–Ziv (LZ78) generalization. We will show how to compress and decompress sequences by utilizing the POLZ. In addition, we establish the methods for determining the upper bounds for sequence complexity by using the notion of equivalence classes. We will also demonstrate that POLZ is asymptotically optimal, achieving an even greater compression rate when compared to the LZ78 when applied to sequences emanating from concurrent processes. Furthermore, we will show that the complexity obtained by using the LZ78 is also an upper bound for the POLZ complexity. We have developed examples in robotics and real-time systems to show how to use and the benefits of using our algorithm for compression within the context of concurrent systems. When the concurrency of the processes is taken into account, all experimental results show a significant compression gain. [ABSTRACT FROM AUTHOR]

This paper presents Adaptive Batch-ParEGO, an adaptive batch Bayesian optimization method for expensive multi-objective problems. This method extends the classical multi-objective Bayesian optimization method, sequential ParEGO, to the batch mode. Specifically, the proposed method exploits a newly proposed bi-objective acquisition function to recommend and evaluate multiple solutions. The bi-objective acquisition function takes exploitation and exploration as two optimization objectives, which are traded off by a multi-objective evolutionary algorithm. Since there's usually a certain number of limited hardware resources available in reality, we further propose an adaptive solution selection criterion to fix the number of candidate solutions in each iteration. This strategy dynamically balances exploitation and exploration by tuning the hyper-parameter in the exploitation-exploration fitness function. In addition, the expected improvement is exploited to select another candidate solution to ensure convergence and make the algorithm more robust. We verify the effectiveness of Adaptive Batch-ParEGO on three multi-objective benchmarks and a hyperparameter tuning task of neural networks compared with the state-of-the-art multi-objective approaches. Our analysis demonstrates that the bi-objective acquisition function with the adaptive recommendation strategy can balance exploitation and exploration well in batch mode for expensive multi-objective problems. All our source codes will be published at https://github.com/thuiar/Adaptive-Batch-ParEGO. [ABSTRACT FROM AUTHOR]

Graph mining has been widely studied to analyze real-world graph properties and applied to various applications. In particular, graph subspace clustering performance, defined as partitioning high-dimensional graph data into several clusters by finding minimum weights for the edges, has been consistently improved by exploiting deep learning algorithms with Euclidean features extracted from Euclidean domains (image datasets). Most subspace clustering algorithms tend to extract features from the Euclidean domain to identify graph characteristics and structures, and hence are limited for real-world data applications in non-Euclidean domains. This paper proposes a self-supervised deep geometric subspace clustering algorithm optimized for non-Euclidean high-dimensional graph data by emphasizing spatial features and geometric structures while simultaneously reducing redundant nodes and edges. Quantitative and qualitative experimental results verified the proposed approach is effective for graph clustering compared with previous state-of-the-art algorithms on public datasets. [ABSTRACT FROM AUTHOR]

In this paper, an efficient double-image visually meaningful encryption algorithm combining a new chaotic system, parallel compressive sensing (PCS) and fast Fourier transform (FFT), is proposed. First, Arnold scrambling is performed on two plain images after wavelet transform. Then, the scrambled secret images are compressed by applying a key-controlled partial Hadamard matrix. Finally, the final cipher image with the same resolution as the plain image is acquired by embedding the two encrypted and measured secret images into a carrier image through FFT. To achieve a higher level of security, a new chaotic system combining infinite collapses and a tent map (ICTM) is introduced for disturbing the strong correlation of images and randomly embedding encrypted data into carrier images. The parameters of ICTM are generated by the hash value of the plain images, which enhances the ability of the cryptographic system to resist plaintext attacks. Moreover, the parameters related to the plaintext are embedded into the alpha channel of the visually meaningful cipher image to reduce unnecessary key transmission. The simulation results and performance analysis show that the cryptosystem is feasible and efficient under the condition of guaranteeing all the evaluation indices. [ABSTRACT FROM AUTHOR]

Graph mining has been widely studied to analyze real-world graph properties and applied to various applications. In particular, graph subspace clustering performance, defined as partitioning high-dimensional graph data into several clusters by finding minimum weights for the edges, has been consistently improved by exploiting deep learning algorithms with Euclidean features extracted from Euclidean domains (image datasets). Most subspace clustering algorithms tend to extract features from the Euclidean domain to identify graph characteristics and structures, and hence are limited for real-world data applications in non-Euclidean domains. This paper proposes a self-supervised deep geometric subspace clustering algorithm optimized for non-Euclidean high-dimensional graph data by emphasizing spatial features and geometric structures while simultaneously reducing redundant nodes and edges. Quantitative and qualitative experimental results verified the proposed approach is effective for graph clustering compared with previous state-of-the-art algorithms on public datasets. [ABSTRACT FROM AUTHOR]

In this paper, an efficient double-image visually meaningful encryption algorithm combining a new chaotic system, parallel compressive sensing (PCS) and fast Fourier transform (FFT), is proposed. First, Arnold scrambling is performed on two plain images after wavelet transform. Then, the scrambled secret images are compressed by applying a key-controlled partial Hadamard matrix. Finally, the final cipher image with the same resolution as the plain image is acquired by embedding the two encrypted and measured secret images into a carrier image through FFT. To achieve a higher level of security, a new chaotic system combining infinite collapses and a tent map (ICTM) is introduced for disturbing the strong correlation of images and randomly embedding encrypted data into carrier images. The parameters of ICTM are generated by the hash value of the plain images, which enhances the ability of the cryptographic system to resist plaintext attacks. Moreover, the parameters related to the plaintext are embedded into the alpha channel of the visually meaningful cipher image to reduce unnecessary key transmission. The simulation results and performance analysis show that the cryptosystem is feasible and efficient under the condition of guaranteeing all the evaluation indices. [ABSTRACT FROM AUTHOR]

• We propose to use fractional order correlation to measure the relationship between two sequences and apply the normalized form of the results to create the fractional order shape-based distance between two sequences. • We use the average sequence calculation method DBA to determine the cluster center and then combine it with our distance to cluster the time series. • We also combine our distance with the center determination strategy of other clustering algorithms to execute comparative experiments. • Experiments show that our method has improved the clustering accuracy. Our proposed distance can achieve better results when combined with a variety of strategies. At the same time, in the shape-based clustering algorithm, compared with the best KShape algorithm, we can also achieve better results. Time series clustering is a research hotspot in data mining. Most of the existing clustering algorithms combine with the classical distance measure which ignore the offset of sequence shape. As a result, shape-based clustering algorithms are becoming increasingly popular. On the majority of data sets, the most representative shape-based clustering algorithm, KShape, which defines a shape-based distance with shift invariance, has been shown to outperform other algorithms. In this paper, we propose a new shape-based clustering algorithm named Fractional Order Shape-based k-cluster(FrOKShape), which defines a multi-variable shape-based distance by normalized fractional order cross-correlation and uses the DTW Barycenter Averaging (DBA) as a center computation strategy. Our distance exhibits excellent shape shift deviating properties and good compatibility integrated with a variety of existing clustering center strategies so that it can provide more potential good results. Experiments show that combining our distance with a traditional clustering algorithm produces excellent clustering indicators. In a series of comparative experiments, FrOKShape also exhibits a comparable result to the existing better shape-based clustering algorithm KShape. [ABSTRACT FROM AUTHOR]

The inverted file (IVF) is one of the most efficient non-exhaustive search algorithms for approximate nearest neighbor (ANN) search. In this paper, we propose a new three-step fast search algorithm in the framework of IVF named fast IVF (fIVF) to accelerate the ANN search. Different from the traditional IVF algorithm that conducts a full search at the coarse quantization stage, we introduce (1) cell-level rejection, (2) vector-level rejection, and (3) subspace-based pyramid model with multi-resolution to speed up this high time-consuming stage. Since our proposed fIVF concentrates on finding the exact w nearest coarse codewords at the coarse quantization stage and follows the same product quantization stage, it can achieve the same search accuracy as the traditional IVF. Experimental results on different datasets demonstrate that our proposed fIVF can significantly speed up the search process while keeping the same search accuracy as the traditional IVF. [ABSTRACT FROM AUTHOR]

• Two closely related nature inspired algorithms are evaluated extensively on a function for which nothing can be proved. • Results show that both of them are similar. • The case of randomized hypercomputation is debunked. • Three theses related to nature inspired algorithms and hypercomputation are proposed. • Antithesis to belief, these theses are meant to be proved (or disproved) and not only meant as theses. The intention of this paper is to empirically compare two closely related (and considered novel metaphor based) nature inspired algorithms and then to use these comparisons and other ideas to shed some light on randomized hypercomputation. The Bat and the Novel Bat algorithms are two of the recently created nature inspired algorithms with the claim of novelty in both of them as both of them uses different randomization, but how novel or similar are they in reality remains hazy especially from the practical perspective. Therefore, here I compare both of them on a data dependent, unexplainable real world classification function. Particularly, I create two classification machines, one derived by BA and another derived by the NBA, using weighted liner loss twin support vector machine and compare it with other trailblazing classifiers on the real world UCI machine learning data sets. These machines are, especially compared with each other also. The results show that the formulated machines perform better with respect to other classifiers on more than 80% of comparisons but are extraordinarily similar between themselves, as they agree with each other up to even four decimal places in almost 100% of cases. Moreover, these optimization algorithms form a perfect example of partially random machines which are claimed to be hypercomputational. But, here it can be shown that unless one has an access to the uncomputable input and uses it intelligently one cannot hypercompute. [ABSTRACT FROM AUTHOR]

• The adaptation of parameter and strategy is realized through accompanying evolution. • Accompanying population and generalized opposition-based learning enhance the population diversity. • Radial spatial projection is used for dynamic analysis of evolutionary direction. Differential evolution (DE) is an intelligent optimization algorithm inspired by biological evolution. Setting a mutation strategy and control parameters that meet the optimization requirements are the premise for DE to achieve good performance. This paper proposes a parameter and strategy adaptive differential evolution algorithm based on accompanying evolution (APSDE). Through the accompanying population, in which individuals are composed of suboptimal solutions, the mutation strategy and control parameters are optimized to realize the adaptation of the strategy and parameters of the main population. Population diversity is enhanced in evolution by generating reverse individuals. In addition, radial spatial projection technology is utilized to track the change in evolution direction with optimization. The performance of APSDE is validated under four sets of benchmark problem suites from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC), and compared with state-of-the-art optimization algorithms. The results show that the proposed algorithm has better optimization performance than the competitive algorithms because of its efficient adaptive mechanism and its excellent population diversity. [ABSTRACT FROM AUTHOR]

PageRank is a classic measure that effectively evaluates the importance of nodes in large graphs. It has been applied in numerous applications spanning data mining, Web algorithms, recommendation systems, load balancing, search and connectivity structures identification. Computing PageRank for large graphs is challenging and this has motivated the studies of distributed algorithms to compute PageRank. Previously, little works have been spent on the distributed PageRank algorithms with strong guarantees on both complexity and accuracy. In this paper, we focus on the theoretical aspect and study the complexity of distributed PageRank computation based on the well-known congested-clique model with a bandwidth generalization. An existing algorithm proposed by Sarma et al. (2015) can be applied in this model, which estimates PageRanks in an n -node graph using, with high probability, O (log n) communication rounds and a bandwidth of O ((log n) 3) bits. We present Radar-Push (RP), which is a distributed PageRank algorithm that is strictly better in round complexities. Specifically, Radar-Push uses O (log log n) communication rounds and an edge bandwidth of O ((log n) 3) bits. We further show that Radar-Push can be adapted to efficiently compute an important variant of PageRank, namely, the batch one-hop personalized PageRank, in O (log log n) communication rounds. [ABSTRACT FROM AUTHOR]

Q-learning (QL) is a popular method for control problems, which approximates the maximum expected action value using the maximum estimated action value, thus it suffers from positive overestimation bias. Various algorithms have been proposed to reduce overestimation bias. But some of these methods cause underestimation bias. Furtherly, about underestimation, which kind of estimators causes underestimation is less understood. In this paper, instead of a certain specific method, we focus on underestimation estimators, especially on K estimates of the action values. We generalize these estimators to propose an Underestimation Estimator Set (UES) and theoretically prove all of the estimators in this set suffer from underestimation bias. We further study the bias properties of these estimators and conclude that their biases are different from each other's and depend on the specific conditions they meet. Thus, our set provides various estimators for QL in different settings. Finally, to better illustrate the properties of estimators, we test the performance of several estimators in our set. Empirical results show that Median estimator (Me) underestimates less than double Q-learning (DQL) and doesn't suffer overestimation as QL, and Min estimator (M1E) underestimates more than DQL. Besides, Me and M1E perform as well as or better than other estimators on some benchmark environments. [ABSTRACT FROM AUTHOR]

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

Clustering analysis is a powerful tool for discovering potential knowledge in datasets. However, numerous existing clustering algorithms suffer from heavy reliance on parameter settings and cannot cluster complex manifold data very well. Moreover, although non-parameter clustering algorithms aim to lower the usage threshold, their actual performance in clustering is often unsatisfactory. Addressing how to achieve similar clustering effects for numerous data points with only a few core data points during clustering is a valuable consideration. To alleviate these challenges, a non-parameter clustering algorithm is proposed and named N on- p arameter C lustering Algorithm based on C hain P ropagation and N atural Neighbor (NPCCPN) in this paper, by jointly using chain propagation and natural neighbor. Specifically, NPCCPN identifies core points through chain propagation and clusters them using the saturated neighborhood graph. This makes the core data extraction and clustering process efficient and non-parameter. Finally, the performance of the method is validated on 15 complex synthetic datasets and 10 real datasets from public UCI database. The experimental results show that the effectiveness and superiority of the proposed algorithm. Purity scores first. ACC scores second without parameters, but the score first algorithm needs to set the parameters. [ABSTRACT FROM AUTHOR]

Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K -means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K -means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results. • AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations. • A fitness function is designed based on the clustering principle of "birds of a feather flock together", with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm. • An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets. [ABSTRACT FROM AUTHOR]

• An improved gaining-sharing knowledge-based algorithm named MSGSK is proposed. • A parameter adjustment strategy is developed to adjust relative parameters of MSGSK. • A backtracking differential mutation strategy is designed to enrich the population diversity. • A strategy selection mechanism is presented to integrate the former two strategies. • MSGSK extracts more accurate parameters for five PV models. The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature. [ABSTRACT FROM AUTHOR]

In a modern distributed learning framework, the speeds of intra-worker calculation and inter-worker communication may differ by 1,000 times. It is advisable to perform expensive calculations on worker machines and communicate as few rounds as possible. In this paper, we propose three novel distributed sparse learning algorithms (EDSL-ET, EDSL-IBCD, and EDSL-IBCD-ET) in high dimensions, which are efficient in both computation and communication. Algorithm EDSL-ET utilizes the Top k sparse technique to reduce communication costs and adopts error feedback to guarantee convergence. Algorithm EDSL-IBCD greatly reduces communication costs by introducing an independent block coordinate descent method. Algorithm EDSL-IBCD-ET takes advantage of EDSL-ET and EDSL-IBCD algorithms for extra-high dimensional feature learning, which is the best one whether in communication, computation, and stability. We give theoretical guarantees of the three algorithms under mild conditions, which match the performance of the centralized algorithm. Extensive experiments on simulated and real data validate our theoretical analysis and demonstrate that the proposed algorithms perform well with just a few rounds of communications. [ABSTRACT FROM AUTHOR]

• Analyze DE algorithm two key issues: parameter sensitivity and search imbalance. • A complementary mutation strategy based on complementary symmetric selection and a Gaussian distribution mutation is proposed. • Data fusion-based parameter adaptation method proposed for parameter rationalization. • A novel variant of DE called DACDE is designed and results shown competitive edge. As an excellent optimization algorithm widely used to solve various practical problems, differential evolution (DE) algorithm has few parameters, yet its performance is significantly affected by these parameters. To address this issue, this paper presents a novel variant of DE called DACDE, which utilizes data fusion-based parameter adaptation and a complementary mutation strategy. Most parameter adaptation methods based on successful history only analyze the mean of parameters and ignore their degree of dispersion. In DACDE, the successful parameter distribution is recorded and described by both the mean and variance. Data fusion is then used to combine records and generate an estimated distribution, which is applied to a Gaussian distribution to generate new parameters. Inspired by opposition-based learning, we introduce a complementary mutation strategy. This strategy employs a symmetric selection mechanism to adapt to the varying search abilities required by the algorithm at different stages. The new variant is verified on 32 single-objective functions from CEC 2011 and 2014 benchmark suites, and the results show that DACDE is competitive compared to other 29 evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL-IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees. [ABSTRACT FROM AUTHOR]

Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. H igh U tility C o-location P attern M ining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like pruning technique does not apply. To address this problem, we firstly suggest two novel pruning strategies to trim the pattern search space. Then, a series of optimizing techniques are presented to speed up the pattern utility ratio calculation of each candidate. Based on above techniques, a fast HUCPM algorithm is proposed, which searches for high utility co-locations involved in each pattern branch via a depth-extending manner and equips with a heuristic strategy to enhance the effect of pruning techniques. Moreover, we theoretically prove the completeness and correctness of the proposed algorithm, and discuss its algorithmic complexity. On multiple spatial datasets, we conduct substantial experiments to reveal the superiority of our algorithm in efficiency and scalability, as well as the effectiveness of the proposed technique. Particularly, the proposed algorithm in this paper runs faster than other baselines for several times to several orders of magnitude. [ABSTRACT FROM AUTHOR]

In association rules mining from data that have numeric-valued attributes, automatically adjusting the attribute intervals at the time of the mining process without a preprocess is very critical for preventing data loss and attribute interactions. In this paper, differential evolution and sine cosine algorithm based novel hybrid multi-objective evolutionary optimization methods are proposed for rapidly and directly mining the reduced high-quality numerical association rules by simultaneously adjusting the relevant intervals of related attributes without finding the frequent itemsets. These algorithms perform a global search and find the high-quality rules set in only one execution by modeling the rule mining task as a multi-objective problem that simultaneously meets different conflicting metrics. The algorithms proposed in this paper ensure the discovered rules to have high confidence and support and to be comprehensible. The proposed methods automate the rule mining process by directly finding the minimum intervals for the attributes and eliminating the need for minimum confidence and minimum support determined beforehand for each data set. The performances of new algorithms proposed in this study were tested with those of the state-of-the-art algorithms. The results show superiority of the proposed methods on the data sets that contain fewer attributes and higher number of instances. [ABSTRACT FROM AUTHOR]

Identifying frequent item-sets is a popular data-mining task. It consists of finding sets of items frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets may be time-consuming, and a user may be interested merely in some specific item-sets rather than all of them. Recently, methods have been proposed for targeted item-set mining; that is to calculate the support of some item-sets of interest. Though this approach is often more suitable for real applications than traditional item-set mining approaches, performance remains an issue. To address that issue, this paper presents a novel algorithm for multitude-targeted mining, named Guided Frequent Pattern-Growth (GFP-Growth). The GFP-Growth algorithm is designed to quickly mine a given set of item-sets using a small amount of memory. This paper proves that GFP-Growth yields the exact frequency-counts for each item-set of interest. It further shows that GFP-Growth can boost the performance for several problems requiring item-set mining. We specifically study the problem of generating minority-class rules from imbalanced data and develop the Minority-Report Algorithm (MRA) that uses GFP-Growth to solve this problem efficiently. We prove several theoretical properties of MRA and present experimental results showing substantial performance gain. [ABSTRACT FROM AUTHOR]

• The paper provides two co-clustering algorithms for distributional-valued data. • The paper uses L2 Wasserstein distances to compare distributional-valued data. • The algorithms compute the weights of relevance of the distributional variables. • The algorithms compute the weights of relevance of the variable's components. • Applications with synthetic and real data sets corroborate the proposed method. This paper is concerned with the co-clustering of distribution-valued data, that is, the simultaneous partitioning of rows and columns of an input data table, the elements of which are distributions (or histograms) representing aggregate data. The first proposed method extends the double k-means algorithm to distributional data. The L 2 Wasserstein distance, also known as Mallow's distance, is used to compare distributions. To consider the different relevance of the variables characterizing the clusters, four variants of adaptive distributional double k-means are proposed. Accordingly, in the co-clustering procedure, an additional step is introduced to compute the relevance weights associated with the variables. In particular, each of the four algorithms provides i) a set of weights for the variables; ii) different sets of weights for the variables, one for each cluster (cluster-wise); iii) a double set of weights for the variables according to the decomposition of the L 2 Wasserstein distance into two components; iv) different double sets of weights for the variables and distance components, one for each cluster (cluster-wise). Applications using simulated and real data demonstrate the effectiveness of the proposed algorithms and the contribution of the relevance weights to the co-clustering procedure according to the structure of the data. [ABSTRACT FROM AUTHOR]

The growing demand of users for video streaming services has brought huge challenges to the streaming media service system. The cooperative edge-cloud computing architecture that combines cloud computing and edge computing can effectively reduce the burden on streaming media service systems. The streaming content caching under the cooperative edge-cloud computing architecture is important to improve the quality of user service. Based on this, this paper proposed a cache strategy based on user mobility and content popularity. In this cache strategy, the Markov model is used to describe the user mobility, the multiple linear regression model is used for content popularity prediction, and the content caching operation is performed according to the user mobility and the content popularity. To reduce the load of edge servers and further improve the quality of user service, this paper proposed a content placement strategy based on marginal gain. In this content placement strategy, a content placement problem is established by considering the content access latency and the content placement cost. Then a content placement algorithm based on marginal gain is proposed to solve the content placement problem. The content placement operation is performed by analyzing the marginal gain brought by placing the contents on edge servers to achieve the optimal performance of content placement. The experimental results show that the proposed strategies can effectively improve the performance in terms of the cache hit ratio, the content access latency, the storage cost and the remote data access cost. [ABSTRACT FROM AUTHOR]

Multi-label classification is a machine learning task to construct a model for assigning an entity in the dataset to two or more class labels. In order to improve the performance of multi-label classification, a multi-objective feature selection algorithm has been proposed in this paper. Feature selection as a preprocessing task for Multi-label classification problems aims to choose a subset of relevant features. Selecting a small number of high-quality features decreases the computational cost and at the same time maximizes the classification performance. However extreme decreasing the number of features causes the failure of classification. As a result, feature selection has two conflicting objectives, namely, minimizing the classification error and minimizing the number of selected features. This paper proposes a multi-objective optimization algorithm to tackle the multi-label feature selection. The task is to find a set of solutions (a subset of features) in a sophisticated large-scale search space using a reference-based multi-objective optimization method. The proposed algorithm utilizes an opposition-based binary operator to generate more diverse solutions. Injection of extreme point of the Pareto-front is another component of the algorithm which aims to find feature subsets with less classification error. The proposed method is compared with two other existing methods on eight multi-label benchmark datasets. The experimental results show that the proposed method outperforms existing algorithms in terms of various multi-objective evaluation measures, such as Hyper-volume indicator, Pure diversity, Two-set coverage, and Pareto-front proportional contribution. The proposed method leads to get a set of well-distributed trade-off solutions which reach less classification error in comparing with competitors, even with the fewer number of features. [ABSTRACT FROM AUTHOR]

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect ; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect , the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV , which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]

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

More training instances could lead to better classification accuracy. However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances that could desirably increase accuracy or at least do not decrease accuracy significantly. There exist many instance selection algorithms, but no single algorithm, in general, dominates the others. Moreover, existing instance selection algorithms do not allow direct controlling of the instance selection rate. In this paper, we present a simple and generic cluster-oriented instance selection algorithm for classification problems. Our proposed algorithm runs an unsupervised K Means Clustering algorithm on the training instances and with a given selection rate, selects instances from the centers and the borders of the clusters. On 24 benchmark classification problems, when very similar percentages of instances are selected by various instance selection algorithms, K Nearest Neighbours classifiers achieve more than 2%–3% better accuracy when using instances selected by our proposed method than when using those selected by other state-of-the-art generic instance selection algorithms. [ABSTRACT FROM AUTHOR]

• Complete consistent matrix corresponding to an IFPR is constructed. • Necessary and sufficient conditions for IFPRs to be multiplicatively consistent are proposed. • Ratio-based deviation matrix that takes an accurate measurement of deviation of every element in an IFPR is constructed. • Every indeterminacy degree of an IFPR is never changed in the revising process. In this paper, we propose a method for improving the multiplicative inconsistency of an intuitionistic fuzzy preference relation (IFPR) without computing any model to derive an underlying priority weight's vector with respect to alternatives. For this, a necessary and sufficient condition for the IFPR to be multiplicative consistent is proposed and proved. Based on it, a ratio-based deviation identifying matrix that takes an accurate measurement of deviation of every element in the IFPR is constructed. We prove that the greater the value of the deviation matrix is, the more inconsistent is its corresponding element in the IFPR and based on it, a convergent iterative algorithm of improving the multiplicative consistency is presented. The algorithm uses the fact that all the indeterminacy degrees of the IFPR are never changed in the revising process of multiplicative inconsistency and as a result, the most inconsistent elements are uniquely determined by suitable elements in the IFPR, respectively. The proposed method makes a great difference from the previous methods that derived the underlying priority weight's vector with respect to alternatives based on the given IFPR for improving consistency. A numerical example is provided to show the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems often load in the multi-scenario environment, and they can be modeled as multi-scenario multi-objective optimization problems (MSMOs). So far, existing research on MSMOs mainly focuses on the specific method in engineering applications, and general methods are quite scarce. This paper develops an integrated information-based evolutionary framework to find a set of scenario-based non-dominated solutions for MSMOs. Sequentially, a scenario-based dominance principle evolutionary algorithm and a decomposition-based evolutionary algorithm are combined with the framework to construct two integration algorithms, respectively. Furthermore, the crucial components in both algorithms are developed under the multi-scenario environment, such as scenario-based constraint handling, scenario-based dominance concept, scenario-based crowded degree and multi-scenario decomposition method. Additionally, we design a novel comprehensive evaluation indicator for MSMOs. Finally, the proposed framework is tested on numerous artificial and engineering problems. Experiments demonstrate that the framework has superiority over four existing state-of-the-art peer competitors concerning the indicator. Two integration algorithms are able to find a set of good scenario-based non-dominated solutions. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important tasks in the field known as 'Exploratory Data Analysis' (EDA). It explores the dependencies hidden in individual data attributes, dividing them from one set into smaller subsets. In this paper, a Parallel Complete Gradient Clustering Algorithm (PCGCA) is proposed. The Complete Gradient Clustering Algorithm (CGCA) provides a natural interpretation combined with no need for assumptions regarding the number of clusters, making it an appealing choice. Moreover, in CGCA, internal optimization procedures point out the parameters influencing the size of clusters. Algorithms based on kernel density estimation can, therefore, be applied for diverse practical scenarios. Another very useful usage is outlier detection – which is especially important in the currently fast-growing data industry. The described algorithm has been validated in terms of both the speed of calculation and the quality of the obtained solution. The quality of the solution was evaluated with the use of eleven clustering indexes calculated on six data sets. In addition, the obtained result was compared with several classical well-known methods of clustering. [ABSTRACT FROM AUTHOR]

• A novel membrane-inspired evolutionary framework with hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. • A novel two-stage evolution strategy algorithm based on the membrane system is proposed as the algorithm in membrane. • The concept of information molecule concentration vector inspired by the binding process of information molecules and receptors is proposed to control the information transfer probability. • Can effectively avoid the negative transfer of knowledge and promote convergence. • Can achieve better performance than the state-of-the-art multitasking MOEAs. In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. [ABSTRACT FROM AUTHOR]

Distributed optimization algorithms have the advantages of privacy protection and parallel computing. However, the distributed nature of these algorithms makes the system vulnerable to external attacks. This paper presents two penalty function based resilient algorithms for constrained distributed optimization under static and dynamic attacks. The objective function of the optimization problem is extended to nonsmooth ones and the convergence of the proposed algorithms in this case are proved under some mild conditions. Simulation experiments are performed and compared with some existing resilient primal-dual optimization algorithms using median-based mean estimator. For static attack, the proposed algorithm has better performance and faster convergence rate in the simulation experiments. For dynamic attack, the proposed algorithm has better performance and robustness in the simulation experiments, which illustrate that the proposed algorithms are more effective. [ABSTRACT FROM AUTHOR]

• An end-to-end framework which naturally incorporates KG into the recommender system. • The first KG-based recommender system combining path-based method and propagation-based method. • A method that can automatically discover the most representative user preference is proposed. • Both High-order relationship and local domain features of users/items are learned and aggregated. • The proposed algorithm achieves competitive results on three real-world datasets. Because a knowledge graph's huge amount of item information can help recommender systems develop user/item representations, it has become the most important source of side information. Regardless of the numerous types of user/item representation approaches used in knowledge graph-based recommendation scenarios, they all have problems. In this paper, we propose a knowledge graph-based multi-context-aware recommendation algorithm for learning user/item representations that combines the advantages of path-based and propagation-based methods. A new concept (i.e., rule) is proposed first, which can be a useful way to characterize the user's preferences. Next, based on user-item interactions, an automatic rule discovery algorithm is proposed that can automatically select the most representative user preferences templates in a given recommendation scenario based on the knowledge graph and user behaviors. Then, the learning of high-order connectivity between long-distance user-item pairs is realized according to these templates. After that, a feature representation method of the local neighborhood characteristics of users and items is introduced to compensate for the defect that the path-based method can only catch the high-order connectivity. The experimental results demonstrate MANN's superiority over eight state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

In the scenario of attack-defense involved with unmanned combat air vehicles (UCAVs), it is often envisioned that a large group of UCAVs is deployed to complete some complex tasks which could be specifically modeled as large-scale matrix games. Solving such matrix games by the traditional linear programming approaches, however, could be quite time-consuming and thus cannot be implemented in real-time which is, in fact, a key requirement for real air combat. On this account, an algorithm, termed as dimensionality reduction based matrix game solving algorithm (DR-MG), is proposed in this paper to solve large-scale matrix games in a timely manner. Our algorithm builds on the technique of dimensionality reduction which inherently finds the convex hull vertices of a vector set. Through establishing the connection between Nash equilibria of the matrix games before and after dimensionality reduction, the proposed algorithm is capable of finding the solutions while only dealing with the matrix game with reduced dimensions. As a consequence, it is expected the time complexity of the proposed algorithm is significantly decreased, and thus the algorithm could be applicable in real air combat. Finally, numerical results are provided to show the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) are promising methods for addressing computationally expensive problems. This paper proposes a multiple surrogates and offspring-assisted differential evolution (MSODE) algorithm for high-dimensional expensive problems. Ensemble models consisting of multiple base models are built based on bagging. The ensemble models contribute toward reducing the variations of predictions and the uncertainty of base models. The algorithm proposes a multiple-offspring evolution strategy in which it generates multiple offspring for each parent individual to enhance the search ability of the population. The appropriate number of offspring was investigated, considering the tradeoff between optimization results and efficiency. MSODE consists of a global prescreening search, local search, and uncertainty prescreening search. The global prescreening search adopts a global ensemble to prescreen promising offspring. The local search accelerates the convergence by searching for the optimum of a local ensemble. The uncertainty prescreening search requires a number of fitness evaluations that slightly deteriorate the results. A comprehensive analysis was conducted to determine the optimal parameter settings. MSODE was compared with meta -heuristic algorithms and SAEAs on a series of benchmark problems. The results show that MSODE behaves better than most algorithms and is competitive against the best ones. [ABSTRACT FROM AUTHOR]

• A new generalized fusion fractal PLFF structure is proposed. • Image cryptosystem is proposed based on new fractal structure. • Exhaustive simulation analyses validate the efficient encryption performance. The design and utilization of suitable fractal structures is one of the prominent areas of security for the protection of digital data. This paper proposes a generalized fusion fractal structure by combining two one-dimensional fractals as seed functions from a larger spectrum of fractal functions. A fusion fractal termed as PLFF is formulated by combining traditional Phoenix and Lambda fractals. Improved randomized phase space, self-similar structure on various magnification scales, and fractional dimension are found in the resultant PLFF fractal. The capacity of PLFF to create a pseudo-random number (PRN) sequence in both integer and binary format is validated by its increased complexity and enhanced chaotic range. The generated PRN sequences feature a significant degree of uncorrelation and randomness. A novel image encryption algorithm based on the new PLFF fractal function is proposed which utilizes a generated PRN sequence as secret key. Standard security evaluations such as histogram variance, NPCR and UACI tests for plain-image sensitivity, key sensitivity, information entropy, pixel correlation, and noise and data loss, etc. are used to analyze the performance of the proposed encryption algorithm. The simulation results revealed performance indicators such as entropies > 7.997, NPCR > 96.6, UACI > 33.5, high throughput of ∼ 6MBps, and highly uncorrelated neighboring pixels in encrypted images. The findings are also compared with some current image encryption schemes, demonstrating that the proposed digital image encryption algorithm performs well. [ABSTRACT FROM AUTHOR]

Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms. [ABSTRACT FROM AUTHOR]

• We propose an improved cross coupled map lattice model. • We design a bit-level image encryption scheme using the proposed model. • Bit-level confusion and diffusion are related to our model and plain-image. • The proposed method in this paper can resist the common attacks. In this paper, an improved cross coupled map lattice (CCML) which is derived from CCML is proposed. The experimental analysis of CCML shows that it exhibits weak or even lose chaotic behaviors in a large range of parameters. To enhance its chaotic performance, the new model changes the internal chaotic map of CCML from the logistics map to the tent map and introduces the module operations to limit the obtained value. The theoretical analysis and experimental results prove that the proposed model has a broad chaotic regime over an extensive range of system parameters, positive Lyapunov exponents, higher information entropies and lower mutual information values when compared to CCML, which are highly suitable for chaos-based image encryption. Moreover, this paper applies that model on a bit-level image encryption scheme. The algorithm consists of three main operations: the generation of secret keys, confusion and diffusion operations that can break the limitation of row and column. To confirm the novel algorithm's effectiveness and safety against the common types of attacks, many experiments are done and security analyses are discussed. It demonstrates the good chaos of the proposed model from the aspect of encryption performance. [ABSTRACT FROM AUTHOR]

• In this paper, proposing a novel design of DE for Knapsack problems is reported. • A dual representation of solutions in continuous and binary is introduced. • New efficient fitness evaluation and repairing method is proposed. • Results demonstrated the efficiency of Novel DE in solving Knapsack problems. • Novel DE defined new solutions, better than best-known ones for 5 large instances. The capability of the conventional differential evolution algorithm to solve optimization problems in continuous spaces has been well demonstrated and documented in the literature. However, differential evolution has been commonly considered inapplicable for several binary/permutation-based real-world problems because of its arithmetic reproduction operator. Moreover, many limitations of the standard differential evolution algorithm, such as slow convergence and becoming trapped in local optima, have been defined. In this paper, a novel technique which makes a simple differential evolution algorithm suitable and very effective for solving binary-based problems, such as binary knapsack ones, is proposed. It incorporates new components, such as representations of solutions, a mapping method and a diversity technique. Also, a new efficient fitness evaluation approach for calculating and, at the same time, repairing knapsack candidate solutions, is introduced. To assess the performance of this new algorithm, four datasets with a total of 44 instances of binary knapsack problems are considered. Its performance and those of 22 state-of-the-art algorithms are compared, with the experimental results demonstrating its superiority in terms of both the quality of solutions and computational times. It is also capable of finding new solutions which are better than the current best ones for five large knapsack problems. [ABSTRACT FROM AUTHOR]

In this paper, a chaotic image encryption algorithm based on the matrix semi-tensor product (STP) with a compound secret key is designed. First, a new scrambling method is designed. The pixels of the initial plaintext image are randomly divided into four blocks. The pixels in each block are then subjected to different numbers of rounds of Arnold transformation, and the four blocks are combined to generate a scrambled image. Then, a compound secret key is designed. A set of pseudosecret keys is given and filtered through a synchronously updating Boolean network to generate the real secret key. This secret key is used as the initial value of the mixed linear-nonlinear coupled map lattice (MLNCML) system to generate a chaotic sequence. Finally, the STP operation is applied to the chaotic sequences and the scrambled image to generate an encrypted image. Compared with other encryption algorithms, the algorithm proposed in this paper is more secure and effective, and it is also suitable for color image encryption. [ABSTRACT FROM AUTHOR]