Graph embedding is a technique for obtaining low-dimensional representations of nodes across diverse networks, which may then be used for various downstream tasks and applications. When it applies to heterogeneous networks, it is hard to handle heterogeneous networks because they usually contain different types of nodes and edges with more semantic and structural information. Recently, contrastive learning has developed as the preferred strategy for dealing with unsupervised heterogeneous graph embedding to reduce the cost of human label annotation. However, most multi-view contrastive learning approaches calculate the model’s loss only based on the mutual dependence between the node representation and graph representation. These approaches ignore that both node attributes and node clustering contain discriminative content. To solve this issue, we propose a model called Multi-Level Discriminator-based Contrastive Learning for Multiplex Networks (MLDCL). This model adopts a multi-level multi-discriminator-based approach that can simultaneously learn the global-level structural information, node-level attribute information, and local-level clustering information. Moreover, an augmentation strategy in the contrast learning process from the spectral domain is proposed to improve the representation and discriminative ability of MLDCL. Numerous tests with node clustering and classification tasks on widely used datasets demonstrate the efficacy of the proposed approach.

In a canonical particle swarm optimization (PSO) algorithm, the fitness is a widely accepted criterion when selecting exemplars for a particle, which exhibits promising performance in simple unimodal functions. To improve a PSO's performance on complicated multimodal functions, various selection strategies based on the fitness value are introduced in PSO community. However, the inherent defects of the fitness-based selections still remain. In this paper, a novelty of a particle is treated as an additional criterion when choosing exemplars for a particle. In each generation, a few of elites and mavericks who have better fitness and novelty values are selected, and saved in two archives, respectively. Hence, in each generation, a particle randomly selects its own learning exemplars from the two archives, respectively. To strengthen a particle's adaptive capability, a multipleinput multiple-output fuzzy logic controller is used to adjust two parameters of the particle, i.e., an acceleration coefficient and a selection proportion of elites. The experimental results and comparisons between our new proposed PSO, named as MFCPSO in this paper, and other 6 PSO variants on CEC2017 test suite with 4 different dimension cases suggest that MFCPSO exhibits very promising characteristics on different types of functions, especially on large scale complicated functions. Furthermore, the effectiveness and efficiency of the fuzzy controlled parameters are discussed based on extensive experiments.

Abstract The great advances in computer vision and natural language processing make significant progress in visual question answering. In the visual question answering task, the visual representation is essential for understanding the image content. However, traditional methods rarely exploit the context information of the visual feature related to the question and the relation‐aware information to capture valuable visual representation. Therefore, a gated relation‐aware model is proposed to capture the enhanced visual representation for desiring answer prediction. The gated relation‐aware module can learn relation‐aware information between the visual feature and the context, and a certain object of an image, respectively. In addition, the proposed module can filter out the unnecessary relation‐aware information through the gate guided by the question semantic representation. The results of the conducted experiments show that the gated relation‐aware module makes a significant improvement on all answer categories.

Abstract The image captioning task has attracted great attention from many researchers, and significant progress has been made in the past few years. Existing image captioning models, which mainly apply attention‐based encoder‐decoder architecture, achieve great developments image captioning. These attention‐based models, however, are limited in the caption generation due to the potential errors resulting from the inaccurate detection of objects and incorrect attention to the objects. To alleviate the limitation, a Variational Joint Self‐Attention model (VJSA) is proposed to learn a latent semantic alignment between the given image and its label description for guiding better image captioning. Unlike the existing image captioning models, VJSA first uses a self‐attention module to encode the effective relationship information of intra‐sequence and inter‐sequences relationships. And then the variational neural inference module learns a distribution over the latent semantic alignment between the image and its corresponding description. In the decoding, the learned semantic alignment guides the decoder to generate the higher quality image caption. The results of the experiments reveal that the VJSA outperforms the compared models, and the performances of various metrics show that the proposed model is effective and feasible in image caption generation.

In differential evolution algorithm (DE), it is a widely accepted method that selecting individuals with higher fitness to generate a mutant vector. In this case, the population evolution is under a fitness-based driving force. Although the driving force is beneficial for the exploitation, it sacrifices performance on the exploration. In this paper, a novelty-hybrid-fitness driving force is introduced to trade off contradictions between the exploration and the exploitation of DE. In the new proposed DE, named as NFDDE, both fitness and novelty values of individuals are considered when choosing individuals to create mutant vectors. In addition, two adaptive scaling factors are proposed to adjust the weights of the fitness-based driving force and the novelty-based driving force, respectively, and then distinct properties of the two driving forces can be effectively utilized. At last, to save computational resources, some individuals with lower novelty are deleted when the population has converged to a certain extent. The comprehensive performance of NFDDE is extensively evaluated by comparisons between it and other 9 state-of-art DE variants based on CEC2017 test suite. In addition, distinct properties of the newly introduced strategies and involved parameters are further confirmed by a set of experiments.

The Capacitated Arc Routing Problem (CARP) is a general and challenging arc routing problem. As the problem size increasing, exact methods are not applicable, and heuristic and meta-heuristic algorithms are promising approaches to solve it. To obtain good performance, parameter values of heuristics or meta-heuristics should be properly set. In recent years, automatic parameter tuning, which includes off-line and online parameter tuning, has attracted considerable attention in the evolutionary computation community. At present, parameters are usually determined through simple off-line parameter tuning, such as empirical analysis or grid search, when designing algorithms for CARP. However, using off-line parameter tuning on CARP has some disadvantages, among which the computational cost is the serious one. This work proposed an online parameter tuning approach using exponential recency-weighted kernel density estimation (ERW-KDE), and combines it with the SAHiD algorithm, which is an hierarchical decomposition based algorithm for CARP, to constitute the online parameter tuned SAHiD (OPT-SAHiD) algorithm. The experimental results show that OPT-SAHiD significantly outperforms the compared algorithms on two CARP benchmark sets owing to the proposed online automatic parameter tuning approach. The proposed online automatic parameter tuning approach based on ERW-KDE not only improves the performance of SAHiD algorithm, but also removes the additional computational overhead required for offline parameter tuning.

Parameter tuning, that is, to find appropriate parameter settings (or configurations) of algorithms so that their performance is optimized, is an important task in the development and application of metaheuristics. Automating this task, i.e., developing algorithmic procedure to address parameter tuning task, is highly desired and has attracted significant attention from the researchers and practitioners. During last two decades, many automatic parameter tuning approaches have been proposed. This paper presents a comprehensive survey of automatic parameter tuning methods for metaheuristics. A new classification (or taxonomy) of automatic parameter tuning methods is introduced according to the structure of tuning methods. The existing automatic parameter tuning approaches are consequently classified into three categories: 1) simple generate-evaluate methods; 2) iterative generate-evaluate methods; and 3) high-level generate-evaluate methods. Then, these three categories of tuning methods are reviewed in sequence. In addition to the description of each tuning method, its main strengths and weaknesses are discussed, which is helpful for new researchers or practitioners to select appropriate tuning methods to use. Furthermore, some challenges and directions of this field are pointed out for further research.

The Capacitated Arc Routing Problem (CARP) is an essential and challenging problem in smart logistics. Parameter tuning is commonly encountered in designing and applying heuristic or meta-heuristic algorithms for CARP. Recently, automatic parameter tuning or hyper-parameter optimization, which focuses on automatically finding an optimal parameter setting of an algorithm for problems at hand, has attracted considerable attention and become popular for addressing parameter tuning problems. This paper studies automatic parameter tuning for advanced algorithms in solving CARP. When designing algorithms for CARP, parameters are usually determined through empirical analysis or following some rules of thumb. This paper uses an automatic parameter tuning approach, that is, Bayesian optimization method, to tune an algorithm called SAHiD, which is a scalable approach based on hierarchical decomposition for large-scale CARP. The experimental results show that the algorithm's performance can be significantly improved with automatic parameter tuning. The tuned SAHiD algorithm obtains better solutions and faster convergence speed than original SAHiD on test CARP instances.

Researchers have widely investigated the fractal property of complex networks, in which the fractal dimension is normally evaluated by box-covering method. The crux of box-covering method is to find the solution with minimum number of boxes to tile the whole network. Here, we introduce a particle swarm optimization box-covering (PSOBC) algorithm based on discrete framework. Compared with our former algorithm, the new algorithm can map the search space from continuous to discrete one, and reduce the time complexity significantly. Moreover, because many real-world networks are weighted networks, we also extend our approach to weighted networks, which makes the algorithm more useful on practice. Experiment results on multiple benchmark networks compared with state-of-the-art algorithms show that this PSOBC algorithm is effective and promising on various network structures.

Different from text-based image retrieval, the content-based image retrieval (CBIR) uses low-level visual features to retrieve images. The question of how to reduce semantic gap between the low level visual features and the high level image semantics is still a difficult problem. This paper uses a comparison-based mechanism based on interactive differential evolution (IDE) to help users retrieve their preferred images in a user-oriented way. The effect of the proposed framework is evaluated, and the performance of the technique is better than that of the relevance feedback (RF) based on feature re-weighting method.

The fractality property are discovered on complex networks through renormalizaiton procedure, which is implemented by box-covering method. The unsolved problem of box-covering method is finding the minimum number of boxes to cover the whole network. Here, we introduce a differential evolution box-covering algorithm based on greedy graph coloring approach. We apply our algorithm on some benchmark networks with different structures, such as a E.coli metabolic network, which has low clustering coefficient and high modularity; a Clustered scale-free network, which has high clustering coefficient and low modularity; and some community networks (the Politics books network, the Dolphins network, and the American football games network), which have high clustering coefficient. Experimental results show that our algorithm can get better results than state of art algorithms in most cases, especially has significant improvement in clustered community networks.

For the purpose of adaptation to a changing environment, immune mutation and memory mechanism in the immune system are introduced in thermodynamic genetic algorithm, which helps to prevent the diversity loss and rapidly track the optimum in dynamic environments. Experimental results on 0/1 dynamic knapsack problems demonstrate the merits of the proposed immune thermodynamic genetic algorithm (ITDGA). Compared with the existing classical primal-dual genetic algorithm (PDGA), this algorithm can maintain better diversity and be more suitable to solve 0-1 dynamic problems.

The financial data are usually volatile and contain outliers. One problem of the standard support vector regression (SVR) for financial time series prediction is that it considers data in a fixed fashion only and lack the robustness to outliers. To tackle this issue, we propose the adaptively weighted support vector regression (AWSVR) model. This novel model is demonstrated to choose the weights adaptively with data. Therefore, the AWSVR can tolerate noise adaptively. The experimental results on three indices: the NASDAQ, the Standard & Poor 500 index (S&P), and the FSTE100 index (FSTE) show its advantages over the standard SVR.

This paper proposes a new class of cellular automata, twi-layer couple cellular automata (TLCCA), with specific application to pseudorandom number generation. TLCCA consists of two layer each of which is a one dimensional CA. Two different rules are selected in the lower-layer CA on account of hybrid CA had more complex behavior. The upper-layer CA is divided into two parts. These two parts have a novel neighbourhood, which called couple-structure neighbourhood. By this neighbourhood, two parts in upper layer interplay with each other. ENT test suites are adopted to test the randomness of PRNG. In order to find a stable PRNG, Entropy, chi-square and serial correlation coefficient and their variability need to be considered. So a multi-objectives optimization algorithm is proposed. The results of experiment indicate that the TLCCA PRNG can obtain credible random number using no less than 48 cells. The merits of TLCCA PRNG are simpler structure, higher efficiency and better robusticity.

Evolvable Hardware(EHW) has been proposed as a promising technology for adaptive systems in last few years. However, in practical applications, evolutionary algorithms(EAs) often need numerous generations to search a new solution. In general, a mistaken system is damaged if it cannot restore in time, so the inefficiency problem has become an obstacle of developing adaptive and evolvable hardware. This paper analyzes how those three factors as genotype, algorithm, and methodology affect the efficiency of the EAs, as well as to what extent of their influence respectively, then proposes parallel and recursive decomposition (PRD) as a new decomposition strategy to accelerate the adaptation process from methodology perspective. Finally, some adaptive combination logical circuits are implemented on Xilinx Virtex-II Pro (XC2VP20) FPGA. The results demonstrate that PRD has more improvement on adaptation speed than some previous strategies.

This paper proposes a new approach named SGMIEC in the field of Estimation of Distribution Algorithm (EDA). While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the Selfish Gene Theory (SG) is deployed in this approach and a Mutual Information and Entropy based Cluster (MIEC) model with an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population. Experimental results on several benchmark problems demonstrate that, compared with BMDA and COMIT , SGMIEC often performs better in convergent reliability, convergent velocity and convergent process.

The Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a new circuit representation method based on a two-layer evolutionary scheme with Genetic Programming (TLGP), which uses a divide-and-conquer approach to evolve the analog circuits. This representation has the desirable property which is more helpful to generate expectant circuit graphs. And it is capable of generating various kinds of circuits by evolving the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this method is efficient.

The non-existence of triangular arbitrage in an efficient foreign exchange markets is widely believed. In this paper, we deploy a forecasting model to predict foreign exchange rates and apply the triangular arbitrage model to evaluate the possibility of an arbitrage opportunity. Surprisingly, we substantiate the existence of triangular arbitrage opportunities in the exchange rate forecasting market even with transaction costs. This also implies the inefficiency of the market and potential market threats of profit-seeking investors. In our experiments, neural network based model with back-propagation (BP-NN) is used for exchange rate forecasting.

Differential evolution (DE) and particle swarm optimization (PSO) are the evolutionary computation paradigms, and both have shown superior performance on complex nonlinear function optimization problems. This paper detects the underlying relationship between them and then qualitatively proves that the two heuristic approaches from different theoretical background are consistent in form. Within the general perspective, the PSO can be regarded as a kind of DE. Inspired by this, a novel variant of DE mixed with particle swarm intelligence (DE-SI) is presented. Comparison experiments involving ten test functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the DE-SI, two versions of DE and two PSO variants. The results from our study show that DE-SI keeps the most rapid convergence rate of all techniques and obtains the global optima for most benchmark problems.