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Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness. [ABSTRACT FROM AUTHOR]

• Prior Knowledge Encoding. We propose a novel knowledge-aware prompt-tuning into verbalizer for biomedical relation extraction that the rich semantic knowledge to solve the problem, which simultaneously transfers entity-node-level and relation-link-level structures across graphs. • Efficient Prompt Design. To the best of our knowledge, this is the first work to successfully perform the knowledge jointly optimizing the representation of a learnable words and learnable relation words in the biomedical graphs. • SOTA Results. BioKnowPrompt outperforms baseline models in five relation extraction benchmark datasets in the biomedical domain, showing its potential to serve as an effective general-purpose representation learning algorithm for biomedical graph data. Domain tuning pre-trained language models (PLMs) with task-specific prompts have achieved great success in different domains. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, which directly bridges the gap between pre-training tasks and various downstream tasks. Large unlabelled corpora in the biomedical domain have been created in the last decade(i.e., PubMed, PMC, MIMIC, and ScienceDirect). In this paper, we introduce BioKnowPrompt, a prompt-tuning PLMs model that has been incorporating imprecise knowledge into verbalizer for biomedical text relation extraction. In particular, we use learnable words and learnable relation words to infuse entity and relation information into quick creation, and we use biomedical domain knowledge constraints to synergistically improve their representation. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks such as relation extraction. BioKnowPrompt has a lot of significant potential in few-shot learning, which outperforms the previous models and achieves state-of-the-art on the 5 datasets. [ABSTRACT FROM AUTHOR]

Federated learning is a privacy-preserving machine learning paradigm that can train a model with decentralized data. Classical federated learning systems are vulnerable to attacks from malicious clients. Although a number of efforts have been made to improve robustness of federated learning, existing solutions are either too costly or attack-specific. In this paper, we propose the Robust Ternary Gradients Aggregation (RTGA) algorithm, which can efficiently handle different attacks using two novel mechanisms. At the client-side, the ternary quantization mechanism compresses gradients using only two bits to store each coordinate of the gradient vector. We adopt the error-feedback mechanism to compensate for the difference between the compressed and actual gradients. At the server-side, the robust aggregation mechanism is designed to detect malicious clients according to the risk score of each client. The risk score is calculated by solving an optimization problem of the divergence among clients. Furthermore, we provide analysis of the compression and robustness properties of our proposed algorithm. Extensive experiments show that, compared with existing algorithms, RTGA can mitigate different types of attacks while costing much less communications. [ABSTRACT FROM AUTHOR]

Positive and unlabeled learning (PU learning) is a kind of problem whose goal is learning a two-classes classifier with little proportion of positive samples and numerous unlabeled samples. A series of studies focus on how to extract most likely negative samples from the unlabeled samples, and then train a classifier with the labeled samples as supervised learning. Previous PU learning methods always ignore the additional information called privileged information, which is just provided during the training process while unavailable during testing. In this paper, we propose a novel self-paced algorithm for PU learning with privileged information (SPUPI). The proposed SPUPI extracts some reliable negative samples from unlabeled samples at first, and then generates weights for the unlabeled samples according to the similarity with each class. After that, it builds a more accurate classifier based on privileged information and similarity weights by self-paced learning. By taking the self-paced learning into training, we can build the model with a few labeled samples from easy to complex. We also solve the problem by transforming the primal problem of the proposed model into its dual problem and achieving the PU classifier. Various experiments on the practical datasets indicate that the SPUPI has a better performance compared with previous methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A two-phase optimization system for solving classification problems. • Combining Auto-ML pipelines to improve overall performance. • Intelligent selection of ensemble methods. • Best results delivered by double-fault measure and 20 or 50 maximum number of base models. Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-world problems through machine learning techniques. These tools tend to be more time consuming than standard machine learning libraries, therefore, exploiting all the available resources to the full is a valuable feature. This paper presents a two-phase optimization system for solving classification problems. The system is designed to produce more robust classifiers by exploiting the different architectures that are generated while solving classification problems with Auto-ML tools, particularly AutoGOAL. In the first phase, the system follows a probabilistic strategy to find the best combination of algorithms and hyperparameters to generate a collection of base models according to certain diversity criteria; and in the second, it follows similar Auto-ML strategies to ensemble those models. The HAHA 2019 challenge corpus and the Adult dataset were used to evaluate the system. The experimental results show that: i) a better solution can be built by ensembling a subset of the already tested models; ii) the performance of ensemble methods depends on the collection of base models used; and, iii) ensuring diversity using the double-fault measure produces better results than the disagreement measure. The source code is available online for the research community. [ABSTRACT FROM AUTHOR]

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

Feature selection plays a crucial role in machine learning and data mining, and improves the performance of learning models by selecting a distinguishing feature subset and eliminating irrelevant features. Existing feature selection methods are mainly used for single-label learning and multi-label learning; however, there are only a few feature selection methods for label distribution learning. Label distribution learning has the "curse of dimensionality" problem, similar to that in multi-label learning. In label distribution learning, the related labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms can not be directly applied to label distribution data, and discretizing the label distribution data into multi-label data would result in the loss of some important supervised information. To solve this problem, a novel feature selection algorithm for label distribution learning is proposed in this paper. The proposed method utilizes neighborhood granularity to explore feature similarity, and it uses a correlation coefficient to generate the label correlations. In addition, sparse learning is used to improve the robustness and control complexity. Experimental results indicate that our proposed method is more effective than five state-of-art feature selection algorithms on twelve datasets, with respect to six representative evaluation measures. [ABSTRACT FROM AUTHOR]

Dynamic adaptive streaming over the HTTP (DASH) standard has been widely adopted by many content providers for online video transmission and greatly improve the performance. Designing an efficient DASH system is challenging because of the inherent large fluctuations characterizing both encoded video sequences and network traces. In this paper, a reinforcement learning (RL)-based DASH technique that addresses user quality of experience (QoE) is constructed. The DASH adaptive bitrate (ABR) selection problem is formulated as a Markov decision process (MDP) problem. Accordingly, an RL-based solution is proposed to solve the MDP problem, in which the DASH clients act as the RL agent, and the network variation constitutes the environment. The proposed user QoE is used as the reward by jointly considering the video quality and buffer status. The goal of the RL algorithm is to select a suitable video quality level for each video segment to maximize the total reward. Then, the proposed RL-based ABR algorithm is embedded in the QoE-oriented DASH framework. Experimental results show that the proposed RL-based ABR algorithm outperforms state-of-the-art schemes in terms of both temporal and visual QoE factors by a noticeable margin while guaranteeing application-level fairness when multiple clients share a bottlenecked network. [ABSTRACT FROM AUTHOR]

• Propose an auto-weighted manifold embedding for multi-view semi-supervised classification. • Present an accelerated method with theoretic guarantee and low computational complexity. • Design practical optimization procedures to solve the problems with fast convergence. Multi-view semi-supervised learning has gained much attention since a great number of unlabeled multi-view data are easy to obtain while few labeled data are available. Accordingly, how to utilize the relationship between labeled and unlabeled data is of significance in multi-view semi-supervised learning. In this paper, we propose an auto-weighted manifold embedding model to address multi-view semi-supervised classification problems, where only a small percentage of labeled data points are used for model training. In the proposed model, data points close to each other in the feature space will be assigned to similar classes in the label space through manifold embedding. Accordingly, the class information of labeled data will be employed in the prediction process for unlabeled data. Moreover, an optimal weight for each view of multi-view data is learned automatically, which enables an encouraging fusion quality for multi-view manifold embedding. To further speed up the calculation process and reduce the computational complexity of the proposed model, an effective accelerated auto-weighted manifold embedding scheme is developed. Besides, theoretical analyses are then provided to indicate a tight approximation bound for the primary manifold embedding method, while the accelerated method is designed by an order decrease of magnitude of computational complexity. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed models over compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, the experimental results are suggestive of positive robustness and promising generalization capacity of the proposed methods. [ABSTRACT FROM AUTHOR]

Nowadays, bike-sharing is available in many cities, solving the problem of the last mile, and it is an environmental-friendly way to commute. However, there is a tidal phenomenon in the bike-sharing system, and the rents/returns of bikes at different stations are unbalanced. Thus, bikes at different stations need to be rebalanced regularly and station-level demand prediction plays an essential role in bike-sharing rebalancing. In this paper, a novel deep graph convolutional network (GCN) model with temporal attention (TAGCN) is proposed for bike check-out/in number prediction of each station. TAGCN can not only model the spatial and temporal dependency between varying stations, but also reflect the influence of different time granularity, which are hour-level, day-level and week-level time periodicity. With the help of well-designed temporal attention mechanism, our model can capture the dynamical temporal correlations and comprehensive spatial patterns in bike check-out/in flow effectively. The proposed model consistently outperforms state-of-the-art methods on four real-world bike-sharing datasets that are four seasons data of Divvy Bike System in Chicago. [ABSTRACT FROM AUTHOR]

Although multi-objective evolutionary subspace clustering approaches have shown promise in handling high-dimensional datasets, their performance is restricted by two main drawbacks. First, their local search strategies have not been well investigated. Second, while exploring the search space, they neglect the useful knowledge from previously solved problems. To tackle these issues, this paper proposes a transfer learning-assisted multi-objective evolutionary clustering framework with decomposition. Firstly, we provide a decomposition-based local search strategy. To capture a comprehensive data structure, this strategy updates the weights of features by considering both the within-class compactness and between-class separation, and spontaneously balances the two properties. Secondly, we develop a knowledge transfer strategy. By transferring search experience from a previously solved clustering problem, the strategy improves the search efficiency, consequently enhances the clustering accuracy of the current problem. It has a closed-form solution and can transfer knowledge across both homogeneous and heterogeneous problems from either different or the same domains. Finally, we conduct an extensive experimental study on the framework by comparing with six representative subspace clustering approaches on a wide range of benchmarks and real-world applications. Results demonstrate the superiority of our framework. [ABSTRACT FROM AUTHOR]

Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Large-scale optimization, solving real high-dimensional problems, has attracted many research interests. Large-scale optimization problems are far more difficult than traditional optimization problems due to their numerous local optimum. In this paper, a principle of maximizing the fitness difference between learners and exemplars is proposed to improve the performance of the optimization algorithm. Then based on the principle, a improved particle swarm optimization algorithm called the "ranking-based biased learning swarm optimizer for large-scale optimization" (RBLSO) is proposed. The proposed RBLSO contains two types of learning strategies, namely, ranking paired learning (RPL) and biased center learning (BCL). In RPL, the worse particles learn peer to peer from the better particles according to their ranks, so then the convergence speed will be accelerated. In BCL, each particle learns from the biased center that is defined as the fitness weighted center of the whole swarm. This operator is utilized to strengthen the explorative ability of the algorithm. To test the performances of the proposed algorithm, we conduct some experiments on the proposed learning mechanism. RBLSO is compared with several state-of-the-art large-scale optimization algorithms on two widely used benchmark function sets, CEC2010 and CEC2013. These two function sets were proposed for the special session and competition on large-scale global optimization held under the Congress on Evolutionary Computation (CEC) 2010 and 2013. Experimental results show that RBLSO is effective in solving large-scale optimization problems. [ABSTRACT FROM AUTHOR]

Abstract In this contribution we consider methods for improving the quality of classification by the k nearest neighborhood classifiers in the case of large number of missing values. The classical version of such classifier is applied, moreover the classifier with the aggregation of certainty coefficients of the individual classifiers with the use of the arithmetic mean, as well as new versions of the classifier with the use of diverse interval-valued aggregation functions are considered. After adding missing values to the data, the quality of the classical method decreases. By contrast, the interval modeling of missing values and application of interval-valued aggregation functions in the classifiers entail a much slower decrease of their quality. The paper includes the results of experiments performed on the data sets from the machine learning repository UCI. [ABSTRACT FROM AUTHOR]

Abstract The decision-theoretic rough set (DTRS) can be regarded as a type of cost-sensitive learning method that incorporates cost functions. Three-way decisions for an object are made in the DTRS model to obtain the minimum Bayesian decision cost, including acceptance, rejection and deferment decisions. However, because the cost matrix in traditional cost-sensitive learning problems does not include the cost for deferment decisions, DTRS cannot be directly used to solve traditional cost-sensitive learning problems. In this paper, we present a multiclass three-way decision-theoretic rough set model that can be directly applied to traditional cost-sensitive learning problems. This model can calculate the corresponding cost for deferment decisions based on the traditional cost matrix. In addition, we propose a multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model. The deferment and rejection samples are reduced and eliminated through multiphase iterative learning. Compared with the existing multiclass cost-sensitive learning methods, our proposed method can achieve higher classification accuracy and lower misclassification cost. [ABSTRACT FROM AUTHOR]

Multicriteria classification refers to classify objects evaluated by a set of criteria to preference-ordered decision classes. Dominance-based rough set approach has been successfully introduced to express and reason inconsistencies with a dominance principle in multicriteria classification problems. Hierarchical attribute values exist extensively within many real-world applications, which provide a hierarchical form to organize, view and analyze data from different perspectives for accommodating the preference variability. In this study, we consider an extension of dominance-based rough set approach by applying an incremental learning technique for hierarchical multicriteria classification while attribute values dynamically vary across different levels of granulations. We formalize the dynamic characteristics of knowledge granules with the cut refinement and coarsening through attribute value taxonomies in the hierarchical multicriteria decision systems. In consequence, incremental algorithms for computing dominance-based rough approximations of preference-ordered decision classes are developed by applying the resulted prior-knowledge as the input, and only recomputing those outputs which depend on the changed attribute values. This paper presents the theoretical foundation of the proposed approach. Example analysis and experimental evaluation are also provided for illustration of the feasibility and efficiency. [ABSTRACT FROM AUTHOR]

Multiple-Instance Learning (MIL) refers to the problem wherein each object is a bag consisting of multiple instances and only the bags' labels are provided. MIL data can contain irrelevant, redundant, and noisy components, which makes feature-extraction preprocessing essential for performance improvement. In this paper, we propose a Multiple-Instance Feature Extraction (MIFE) framework to design algorithms at both the bag and instance levels based on the Maximum Trace-Difference criterion, which simultaneously maximizes between-class scattering and minimizes within-class scattering. MIFE not only treats the existing Multiple-Instance Discriminant Analysis algorithm as an instance-level realization but also enables us to adopt different bag-level distances to design corresponding bag-level algorithms. In particular, we introduce the Class-to-Bag (C2B) and Bag-to-Bag (B2B) distances into the MIFE framework and obtain the MIFE-C2B and MIFE-B2B algorithms, respectively. The experimental results show that both MIFE-C2B and MIFE-B2B obtain competitive classification performance, and MIFE-B2B obtains the best performance on most tested datasets. The dimensionality reduction results show that both MIFE-C2B and MIFE-B2B obtain their best performance with no more than approximately 30% of the original dimensions on most tested datasets. [ABSTRACT FROM AUTHOR]

In this paper, a novel recombination operator, called adaptive hybrid crossover operator (AHX), is designed for tackling continuous multiobjective optimization problems (MOPs), which works effectively to enhance the search capability of multiobjective evolutionary algorithms (MOEAs). Different from the existing hybrid operators that are commonly operated on chromosome level, the proposed operator is executed on gene level to combine the advantages of simulated binary crossover (SBX) with local search ability and differential evolution (DE) with strong global search capability. More opportunities are assigned to DE in the early evolutionary stage for gene-level global search in decision space; whereas, with the generation grows, more chances are gradually allocated to SBX for gene-level local search. The balance between the gene-level global and local search is well maintained by an adaptive control approach in AHX. To validate the effectiveness of AHX, it is studied by substituting the original recombination operators in the four state-of-the-art MOEAs (i.e., NSGA-II, SPEA2, SMS-EMOA, and MOEA/D), and the performance of revised algorithms is significantly improved. Furthermore, AHX is also compared to three recently proposed recombination operators, such as a newly DE inspired (DEI) recombination operator, a learning paradigm based on jumping genes (JGBL) and a bandit-based adaptive operator selection approach (FRRMAB). The experimental studies validate that AHX can be effectively integrated into different frameworks of MOEAs, and performs better than SBX, DE, DEI, JGBL and FRRMAB in solving various kinds of MOPs. [ABSTRACT FROM AUTHOR]

When memory-based evolutionary algorithms are applied in dynamic environments, the certainly use of uncertain prior knowledge for future environments may mislead the evolutionary algorithms. To address this problem, this paper presents a new, memory-based evolutionary approach for applying the Bayesian optimization algorithm (BOA) in dynamic environments. Our proposed method, unlike existing memory-based methods, uses the knowledge of former environments probabilistically in future environments. For this purpose, the run of BOA is modeled as the movements in a Markov chain, in which the states become the Bayesian networks that are learned in every generation. When the environment changes, a stationary distribution of the Markov chain is defined on the basis of the retrieved prior knowledge. Then, the transition probabilities of BOA in the Markov chain are modified (biased) to comply with the defined stationary distribution. To this end, we employ the Metropolis algorithm and modify the K2 algorithm for learning the Bayesian network in BOA in order to reflect the obtained transition probabilities. Experimental results show that the proposed method achieves improved performance compared to conventional methods, especially in random environments. [ABSTRACT FROM AUTHOR]

Feedforward neural networks with random weights (FNNRWs), as random basis function approximators, have received considerable attention due to their potential applications in dealing with large scale datasets. Special characteristics of such a learner model come from weights specification, that is, the input weights and biases are randomly assigned and the output weights can be analytically evaluated by a Moore–Penrose generalized inverse of the hidden output matrix. When the size of data samples becomes very large, such a learning scheme is infeasible for problem solving. This paper aims to develop an iterative solution for training FNNRWs with large scale datasets, where a regularization model is employed to potentially produce a learner model with improved generalization capability. Theoretical results on the convergence and stability of the proposed learning algorithm are established. Experiments on some UCI benchmark datasets and a face recognition dataset are carried out, and the results and comparisons indicate the applicability and effectiveness of our proposed learning algorithm for dealing with large scale datasets. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic mentoring scheme along with a self-regulation scheme have been incorporated in the standard Particle Swarm Optimization (PSO) algorithm to empower the searching particles with human-like characteristics. The algorithm is referred to as a Dynamic Mentoring and Self-Regulation based Particle Swarm Optimization (DMeSR-PSO) algorithm. Based on their experiences, the particles are divided into three groups, viz., the mentor group, the mentee group and the independent learner group where the number of particles in each group is dynamically changing in every iteration. In human learning psychology, mentoring is regarded as a powerful and effective learning process and independent learners are the ones who do not need mentoring and are capable of performing self-regulation of their own knowledge. Therefore, the particles in each of the above three groups have different learning strategies for their velocity updates where the mentors are equipped with a strong self-belief based search, the mentees are taking guidance from the mentors and the independent learners employ self-perception strategy. The DMeSR-PSO algorithm has been extensively evaluated using the simple unimodal and multimodal benchmark functions from CEC2005, more complex shifted and rotated benchmark functions fromCEC2013 and also based on eight real- world problems from CEC2011. The results have been compared with six state-of-the-art PSO variants and five meta-heuristic algorithms for the CEC2005 problems. Further, a comparative analysis on CEC2013 benchmark functions with different PSO variants has also been presented. Finally, DMeSR-PSO's performance on the real-world problems is compared with the top two algorithms from the CEC2011 competition. The results indicate that the proposed learning strategies help DMeSR-PSO to achieve faster convergence and provide better solutions in most of the problems with a 95% confidence level, yielding an effective optimization algorithm for real-world applications. [ABSTRACT FROM AUTHOR]

Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the Algorithm Selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning; that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects; and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks. [ABSTRACT FROM AUTHOR]

Modeling dynamical systems is a commonly used technique to solve partially observable problems in the artificial intelligence field. Predictive state representations (PSRs) have been proposed as an alternative to partially observable Markov decision processes (POMDPs) to model dynamical systems. Although POMDPs and PSRs provide general frameworks to solve partially observable problems, they rely heavily on a known and accurate model of the environment. However, in real world applications it is extremely difficult to build an accurate model. In this paper, we propose an algorithm to solve partially observable problems using an inaccurate PSR model which is learned from samples. The proposed algorithm can also improve the accuracy of the learned model. Given the inaccurate PSR model, the PSR state is identified firstly. Then the traditional Markov decision processes (MDP) techniques are used to solve the partially observable problem. Furthermore, the learned model, which may get off-track as often happens when the model is learned from samples, can be reset. The effectiveness of our proposed algorithm is demonstrated based on a standard set of POMDP test problems. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, particle swarm optimization (PSO) algorithm has been used to solve global optimization problems. This algorithm is widely used as an effective optimization tool in various applications. However, traditional PSO consists of only two searching layers and thus often results in premature convergence into the local minima. Thus, multi-layer particle swarm optimization (MLPSO) is proposed in this paper to improve the performance of traditional PSO by increasing the two layers of swarms to multiple layers. The MLPSO strategy increases the diversity of searching swarms to improve its performance when solving complex problems. The experiment indicates that the novel approach improves the final results and the convergence speed. [Copyright &y& Elsevier]