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Abstract The increasing number of users participating in location-based social networks has resulted in an information overload problem. Recommendation is a process that can free users from this dilemma. Most algorithms either ignore geographical and social properties, or require a tunable coefficient to determine the effect of each property on the outcome. Simultaneously combining these properties has proven to be a challenge. In this paper, we propose a two-step personalized location recommendation that is based on a multi-objective immune algorithm. It can simultaneously optimize the matching qualities of similarity and geographic properties as two functions, thereby providing location recommendations by improving one desired objective without detracting from the other. In the process, each list provides a different compromise between the similarity of check-in preferences and the geographical influence of the user. The user is offered choices from a set of lists that are compiled from the individual's selection of the various tradeoffs. The advantage of this algorithm is that it can recommend user lists without the need to tune any of the weighting coefficients. Experiments performed using the actual data demonstrated that the proposed algorithm is promising and is an effective means for providing accurate recommendations for a user's desired location. [ABSTRACT FROM AUTHOR]

In this paper, an improved quantum-behaved particle swarm optimization (CL-QPSO), which adopts a new collaborative learning strategy to generate local attractors for particles, is proposed to solve nonlinear numerical problems. Local attractors, which directly determine the convergence behavior of particles, play an important role in quantum-behaved particle swarm optimization (QPSO). In order to get a promising and efficient local attractor for each particle, a collaborative learning strategy is introduced to generate local attractors in the proposed algorithm. Collaborative learning strategy consists of two operators, namely orthogonal operator and comparison operator. For each particle, orthogonal operator is used to discover the useful information that lies in its personal and global best positions, while comparison operator is used to enhance the particle’s ability of jumping out of local optima. By using a probability parameter, the two operators cooperate with each other to generate local attractors for particles. A comprehensive comparison of CL-QPSO with some state-of-the-art evolutionary algorithms on nonlinear numeric optimization functions demonstrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the vigorous development of big data, the task of large-scale community structure detection is more challenging. In this paper, a large-scale community detection method based on core node and layer-by-layer label propagation is proposed, which is further extended to the detection of overlapping community structures. First, the core nodes whose node degree is greater than the average degree in the graph are found to make effective use of the feature that the core node is the potential community center. This will also avoid the impact of nodes with low node degrees on community structure detection. Then, starting from the core node, label propagation is carried out layer-by-layer according to the node degree and node connection, which effectively improves the accuracy of community detection. The node labels after label propagation are calibrated according to the current attraction of the community to the nodes, which effectively improves the situation of misclassification in the early community detection. Finally, overlapping community detection is carried out based on the non-overlapping community structure. At this time, the overlapping community detection result is more accurate and interpretable. The community detection results on 3 synthetic networks and 12 real datasets show that the proposed algorithm has more advantages than four non-overlapping community detection methods and two overlapping community detection methods. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic-context cooperative quantum-behaved particle swarm optimization algorithm. The proposed algorithm incorporates a new method for dynamically updating the context vector each time it completes a cooperation operation with other particles. We first explain how this leads to enhanced search ability and improved optimization over previous methods, and demonstrate this empirically with comparative experiments using benchmark test functions. We then demonstrate a practical application of the proposed method, by showing how it can be applied to optimize the parameters for Otsu image segmentation for processing medical images. Comparative experimental results show that the proposed method outperforms other state-of-the-art methods from the literature. [ABSTRACT FROM AUTHOR]

Abstract: The existence of infeasible solutions makes it very difficult to handle constrained optimization problems (COPs) in a way that ensures efficient, optimal and constraint-satisfying convergence. Although further optimization from feasible solutions will typically lead in a direction that generates further feasible solutions, certain infeasible solutions can also provide useful information about the optimal direction of improvement for the objective function. How well an algorithm makes use of these two solutions determines its performance on COPs. This paper proposes a novel selection evolutionary strategy (NSES) for constrained optimization. A self-adaptive selection method is introduced to exploit both informative infeasible and feasible solutions from a perspective of combining feasibility with multi-objective problem (MOP) techniques. Since the global optimal solution of a COP is a feasible non-dominated solution, both non-dominated solutions with low constraint violation and feasible ones with low objective values are beneficial to an evolution process. Thus, the exploration and exploitation of both of these two kinds of solutions are preferred during the selection procedure. Several theorems and properties are given to prove the above assertion. Furthermore, the performance of our method is evaluated using 22 well-known benchmark functions. Experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of the speed of finding feasible solutions and the stability of converging to global optimal solutions. In particular, when dealing with problems that have zero feasibility ratios and more than one active constraint, our method provides feasible solutions within fewer fitness evaluations (FES) and converges to the optimal solutions more reliably than other popular methods from the literature. [Copyright &y& Elsevier]

Abstract: In networked data, linked objects tend to belong to the same class, and densely linked subgraphs are often available. Based on these facts, this paper presents a regularization framework that consists of fitting and regularization terms for transductive learning in networked data. The desirable value of the fitting term is related to the number of labeled data, whereas that of the regularization term is dependent on the structure of the graph. The ratio of these two desirable values is essential for the estimation of the optimal regularization parameters, such as that proposed in our paper. Under the proposed regularization framework, an effective classification algorithm is developed. Two methods are also introduced to incorporate contents of objects into the proposed framework to ultimately improve classification accuracy. Promising experimental results are reported on a toy problem and a paper classification task. [Copyright &y& Elsevier]

Abstract: Inspired by the principle of gene transposon proposed by Barbara McClintock, a new immune computing algorithm for automatic clustering named as Gene Transposon based Clone Selection Algorithm (GTCSA) is proposed in this paper. The proposed algorithm does not require a prior knowledge of the number of clusters; an improved variant of the clonal selection algorithm is used to determine the satisfied number of clusters and the appropriate partitioning of the data set as well. In addition, a novel operation called antibody gene transposon is introduced to the framework of clonal selection algorithm which can realize to find the satisfied number of cluster automatically. The proposed method has been extensively compared with iterated local search approach (ILS) and three well-known automatic clustering algorithms, including automatic clustering using an improved differential evolution algorithm (ACDE); variable-string-length genetic algorithm based clustering techniques (VGA) and the dynamic clustering approach based on particle swarm optimization (DCPSO). 23 datasets with widely varying characteristics are used to demonstrate the superiority of the GTCSA. In addition, GTCSA is applied to a real world application, namely natural image segmentation, with a good performance obtained. [Copyright &y& Elsevier]

Abstract: Artificial immune systems (AIS) are the computational systems inspired by the principles and processes of the vertebrate immune system. AIS-based algorithms typically mimic the human immune system’s characteristics of learning and adaptability to solve some complicated problems. Here, an artificial immune multi-objective optimization framework is formulated and applied to synthetic aperture radar (SAR) image segmentation. The important innovations of the framework are listed as follows: (1) an efficient and robust immune, multi-objective optimization algorithm is proposed, which has the features of adaptive rank clones and diversity maintenance by K-nearest-neighbor list; (2) besides, two conflicting, fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and (3) moreover, an effective, fused feature set for texture representation and discrimination is constructed and researched, which utilizes both the Gabor filter’s ability to precisely extract texture features in low- and mid-frequency components and the gray level co-occurrence probability’s (GLCP) ability to measure information in high-frequency. Two experiments with synthetic texture images and SAR images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms: fuzzy C-means (FCM), single-objective genetic algorithm (SOGA), self-organizing map (SOM), wavelet-domain hidden Markov models (HMTseg), and spectral clustering ensemble (SCE). Experimental results show the proposed framework has obtained the better performance in segmenting SAR images than other five algorithms and behaves insensitive to the speckle noise. [Copyright &y& Elsevier]

Abstract: Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization. [Copyright &y& Elsevier]

The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator-based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization. [ABSTRACT FROM AUTHOR]

• A spatial kernel generation module is proposed. • A spectral kernel generation module is proposed. • A multi-scale spectral channel attention mechanism is proposed. • A strategy for feature fusion through convolution is proposed. • Our network structure has better classification results than the state-of-the-art. With the widespread use of deep learning methods, more and more classification models based on hyperspectral images (HSI) have been continuously proposed. However, due to the characteristics of high dimensionality and low resolution of HSI, the traditional convolutional neural network (CNN) model cannot effectively process it. In this paper, we propose a classification network, called spatial and spectral kernels generation network (SSKNet), to generate spatial and spectral convolution kernels based on image characteristics, and use them to replace the traditional initialization convolution kernels. We can obtain representative kernels with stronger spatial correlation with the region segmentation, clustering, and mapping operations of the spatial kernels generation module (SaKG Module). Simultaneously, we also propose a spectral kernels generation module (SeKG module), which integrates the multi-scale correlation characteristics of different bands into the spectral attention mechanism, making the generated spectral kernels more accurate. Combining the spatial and spectral kernels allows the network to extract salient features and achieve feature fusion efficiently. Experimental results based on multiple HSI data sets show that the proposed method has better classification accuracy and generalization performance than existing methods. [ABSTRACT FROM AUTHOR]

Deep belief network (DBN) is an import deep learning model and restricted Boltzmann machine (RBM) is one of its basic models. The traditional DBN and RBM have numerous redundant features. Hence an improved strategy is required to perform sparse operations on them. Previously, we have proposed our own sparse DBN (SDBN): using a multi-objective optimization (MOP) algorithm to learn sparse features, which solves the contradiction between the reconstruction error and network sparsity of RBM. Due to the optimization algorithm and millions of parameters of the network itself, the training process is difficult. Therefore, in this paper, we propose an efficient parallel strategy to speed up the training of SDBN networks. Self-adaptive Quantum Multi-objectives Evolutionary algorithm based on Decomposition (SA-QMOEA/D) that we have proposed as the multi-objective optimization algorithm has the hidden parallelism of populations. Based on this, we not only parallelize the DBN network but also realize the parallelism of the multi-objective optimization algorithm. In order to further verify the advantages of our approach, we apply it to the problem of facial expression recognition (FER). The obtained experimental results demonstrate that our parallel algorithm achieves a significant speedup performance and a higher accuracy rate over previous CPU implementations and other conventional methods. [ABSTRACT FROM AUTHOR]

• A new multi-layer interaction preference based multi-objective optimization algorithm through decomposition (MLIP-MOEA/D) is proposed. • A fast way to get preference region is established, during the selection process, the preference weight vector is redefined using the angle-based method. • A novel multi-layer interaction strategy for searching the accurate solutions is proposed. During evolution, the preferred region is reduced gradually, making it easier for user to get the final preferred solution(s). • Besides ZDT problems with two objectives, the proposed algorithm shows its potentiality for solving many-objective problems, i.e., DTLZ problems with 3, 5, 8, 10 objectives. Many problems in real world have not only one objective to be met. In the majority of cases, a set of trade-off solutions which spread evenly along the entire Pareto optimal front are generated by multi-objective evolutionary algorithms (MOEAs). Taking the preference of decision maker (DM) into consideration, some specified solutions can be obtained, which is of great interest in practical applications. In this paper, a novel multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition (denoted as MLIP-MOEA/D) is proposed. In MLIP-MOEA/D, a multi-layer interactive strategy is developed during evolution, in the first-layer interaction, the DM will provide a reference vector and an initial radius to determine a preference range, then all solutions in this range will be updated. The algorithm will stop if the DM is satisfied with the first output result, otherwise it will go on to the second-layer interaction. In this step, the most preferred solution generated from the first-layer interaction will be chosen as the new preference direction, and the weight vector is redefined by the angle-based method, and the range of preferred region is reduced gradually, until the closest solution that meet the DM's need is found. The algorithm is tested on a set of benchmark problems including DTLZ problems with more than three objectives, the experimental studies show that the proposed algorithm can effectively search the preferred solutions with the preference information and successfully deal with many-objective optimization problems. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SR) reconstruction is an ill-posed inverse problem because the high-resolution (HR) image, obtained from the low-resolution (LR) image, is non-unique or unstable. In this paper, single image SR reconstruction is treated as an optimization problem, and a new single image SR method, based on a genetic algorithm and regularization prior model, is proposed. In the proposed method, the optimization problem is constructed with a regularization prior model which consists of the non-local means (NLMs) filter, total variation (TV) and adaptive sparse domain selection (ASDS) scheme for sparse representation. In order to avoid local optimization, we combine the genetic algorithm and the iterative shrinkage algorithm to deal with the regularization prior model. Compared with several other state-of-the-art algorithms, the proposed method demonstrates better performances in terms of both numerical analysis and visual effect. [ABSTRACT FROM AUTHOR]

High-order graph matching aims at establishing correspondences between two sets of feature points using high-order constraints. It is usually formulated as an NP-hard problem of maximizing an objective function. This paper introduces a discrete particle swarm optimization algorithm for resolving high-order graph matching problems, which incorporates several re-defined operations, a problem-specific initialization method based on heuristic information, and a problem-specific local search procedure. The proposed algorithm is evaluated on both synthetic and real-world datasets. Its outstanding performance is validated in comparison with three state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Social computing is a new paradigm for information and communication technology. Social network analysis is one of the theoretical underpinnings of social computing. Community structure detection is believed to be an effective tool for social network analysis. Uncovering community structures in social networks can be regarded as clustering optimization problems. Because social networks are characterized by dynamics and huge volumes of data, conventional nature-inspired optimization algorithms encounter serious challenges when applied to solving large-scale social network clustering optimization problems. In this study, we put forward a novel particle swarm optimization algorithm to reveal community structures in social networks. The particle statuses are redefined under a discrete scenario. The status updating rules are reconsidered based on the network topology. A greedy strategy is designed to drive particles to a promising region. To this end, a greedy discrete particle swarm optimization framework for large-scale social network clustering is suggested. To determine the performance of the algorithm, extensive experiments on both synthetic and real-world social networks are carried out. We also compare the proposed algorithm with several state-of-the-art network community clustering methods. The experimental results demonstrate that the proposed method is effective and promising for social network clustering. [ABSTRACT FROM AUTHOR]

Image segmentation is an important and well-known ill-posed inverse problem in computer vision. It is a process of assigning a label to each pixel in a digital image so that pixels with the same label have similar characteristics. Chan–Vese model which belongs to partial differential equation approaches has been widely used in image segmentation tasks. Chan–Vese model has to optimize a non-convex problem. It usually converges to local minima. Furthermore, the length penalty item which is critical to the final results of Chan–Vese model makes the model be sensitive to parameter settings and costly in computation. In order to overcome these drawbacks, a novel bi-convex fuzzy variational image segmentation method is proposed. It is unique in two aspects: (1) introducing fuzzy logic to construct a bi-convex object function in order to simplify the procedure of finding global optima and (2) efficiently combining the length penalty item and the numerical remedy method to get better results and to bring robustness to parameter settings and greatly reduce computation costs. Experiments on synthetic, natural, medical and radar images have visually or quantitatively validated the superiorities of the proposed method compared with five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Capacitated Arc Routing Problem (CARP) has drawn much attention during the last few years. In addition to the goal of minimizing the total cost of all the routes, many real-world applications of CARP also need to balance these routes. The Multi-objective CARP (MO-CARP) commonly exists in practical applications. In order to solve MO-CARP efficiently and accurately, this paper presents a Multi-population Cooperative Coevolutionary Algorithm (MPCCA) for MO-CARP. Firstly, MPCCA applies the divide-and-conquer method to decompose the whole population into multiple subpopulations according to their different direction vectors. These subpopulations evolve separately in each generation and the adjacent subpopulations can share their individuals in the form of cooperative subpopulations. Secondly, multiple subpopulations are used to search different objective subregions simultaneously, so individuals in each subpopulation have a different fitness function, which can be modeled as a Single Objective CARP (SO-CARP). The advanced MAENS approach for single-objective CARP can be used to search each objective subregion. Thirdly, the internal elitism archive is used to construct evolutionary pool for each subregion, which greatly speeds up the convergence. Lastly, the fast nondominated ranking and crowding distance of NSGA-II are used for selecting the offspring and keeping the diversity. MPCCA is tested on 91 CARP benchmarks. The experimental results show that MPCCA obtains better generalization performance over the compared algorithms. [Copyright &y& Elsevier]

Abstract: In this paper we propose a novel Dictionary Learning and Sparse Representation-based Classifier (DLSRC) for image segmentation. In DLSRC, instances-based learning is adopted to find representative dictionaries that can sparsely code various classes of prototype samples in images. Then an incremental version of DLSRC, IDLSRC, is advanced for incremental learning of accumulating knowledge obtained from labeled data. The unsupervised clustering algorithm provides initial labeled samples, and then the labels of candidate samples are incrementally predicted by defining a consistency-enhanced evaluation function. Some experiments are taken on both the artificial texture images and real Synthetic Aperture Radar (SAR) images, to investigate the performance of DLSRC and IDLSRC. Some aspects including (1) the comparison of DLSRC with the Sparse Representation based Classifier (SRC) and some unsupervised clustering approaches, (2) the comparison of IDLSRC with DLSRC, are tested, and the results prove the superiority of our proposed method to its counterparts. [Copyright &y& Elsevier]

Abstract: Recent results of Compressive Sampling (CS) have demonstrated its feasibility in high-resolution radar targets estimation and imaging [2,10,14,15,17,19,23,29,30,32–34]. However, the signal recovery is reduced to seeking a sparse solution to an underdetermined linear system of equations. It is potentially very difficult because even finding a solution that approximates the true minimum is NP-hard. In this paper, we introduce Memetic Algorithm (MA) to solve this non-convex l 0-norm minimization problem, and design a compressive receiver for high-resolution range-reflectivity estimation of multiple radar targets. A double-population MA is proposed, where the position population is used to evaluate the ranges, and the coefficient population is used to realize a local search of target reflectivities. By combining the global search with a local searching operation to exploit the available knowledge in the recovery, the proposed MA outperforms the general purpose optimization algorithms in terms of the quality of solution. Some experiments are taken to investigate the performance of this compressive receiver at different sampling rates, and the results show the superiority to its counterparts in both noiseless environment and noisy, cluttered environment. [Copyright &y& Elsevier]