Laizhong Cui

When big data meets software-defined networking: SDN for big data and big data for SDN

Both big data and software-defined networking (SDN) have attracted great interests from both academia and industry. These two important areas have traditionally been addressed separately in the most of previous works. However, on the one hand, the good features of SDN can greatly facilitate big data acquisition, transmission, storage, and processing. On the other hand, big data will have profound impacts on the design and operation of SDN. In this paper, we present the good features of SDN in solving several issues prevailing with big data applications, including big data processing in cloud data centers, data delivery, joint optimization, scientific big data architectures and scheduling issues. We show that SDN can manage the network efficiently for improving the performance of big data applications. In addition, we show that big data can benefit SDN as well, including traffic engineering, cross-layer design, defeating security attacks, and SDN-based intra and inter data center networks. Moreover, we discuss a number of open issues that need to be addressed to jointly consider big data and SDN in future research.

Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations

Differential evolution (DE) algorithm has been shown to be a very effective and efficient approach for solving global numerical optimization problems, which attracts a great attention of scientific researchers. Generally, most of DE algorithms only evolve one population by using certain kind of DE operators. However, as observed in nature, the working efficiency can be improved by using the concept of work specialization, in which the entire group should be divided into several sub-groups that are responsible for different tasks according to their capabilities. Inspired by this phenomenon, a novel adaptive multiple sub-populations based DE algorithm is designed in this paper, named MPADE, in which the parent population is split into three sub-populations based on the fitness values and then three novel DE strategies are respectively performed to take on the responsibility for either exploitation or exploration. Furthermore, a simple yet effective adaptive approach is designed for parameter adjustment in the three DE strategies and a replacement strategy is put forward to fully exploit the useful information from the trial vectors and target vectors, which enhance the optimization performance. In order to validate the effectiveness of MPADE, it is tested on 55 benchmark functions and 15 real world problems. When compared with other DE variants, MPADE performs better in most of benchmark problems and real-world problems. Moreover, the impacts of the MPADE components and their parameter sensitivity are also analyzed experimentally. Three novel mutation strategies are run in three sub-populations respectively.A novel adaptive strategy is presented to tune the systemic parameters.A simple replacement strategy is designed to remain good solutions.

Artificial bee colony algorithm with gene recombination for numerical function optimization

Display Omitted An improved foraging model is designed for ABC, which can make some employed bees with high quality food source exchange information with each other.A concreted gene recombination operator (GRO) is established by recombining the different superior genes of different good individuals for generating better offspring.GRO is embedded into nine ABC methods for performance evaluation. The experimental results on 22 benchmark functions demonstrate that GRO could enhance the performance of ABC and ABC variants. Artificial bee colony (ABC) algorithm is a stochastic and population-based optimization method, which mimics the collaborative foraging behaviour of honey bees and has shown great potential to handle various kinds of optimization problems. However, ABC often suffers from slow convergence speed since its internal mechanism and solution search equation do well in exploration, but badly in exploitation. In order to solve this knotty issue, inspired by the natural phenomenon that the good individuals (solutions) always contain good genes (variables) and the effective combination of the superior genes from different good individuals could more easily produce better offspring, we introduce a novel gene recombination operator (GRO) into ABC to accelerate convergence. To be specific, in GRO, a part of good solutions in the current population are selected to produce candidate solutions by the gene combination. Especially, each good solution recombines with only one other good solution to generate only one candidate solution. In addition, GRO will be launched at the end of each generation. In order to validate its efficiency and effectiveness, GRO is embedded into nine versions of ABC, i.e., the original ABC, GABC, best-so-far ABC(BSFABC), MABC, CABC, ABCVSS, qABC, dABC and distABC, while yields GRABC, GRGABC, GRBSFABC, GRMABC, GRCABC, GRABCVSS, GRqABC, GRdABC and GRdistABC respectively. The experimental results on 22 benchmark functions demonstrate that GRO could enhance the exploitation ability of ABCs and accelerate convergence without loss of diversity.

A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

A depth-first search (DFS) framework is designed for ABC.Two novel search equations are invented respectively in employed and onlooker bee phases.Our algorithm is better than other ABC variants and non-ABC methods on many benchmark functions. Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tradeoff between exploration and exploitation, in this paper, we propose a depth-first search (DFS) framework. The key feature of the DFS framework is to allocate more computing resources to the food sources with better quality and easier to be improved for evolution. We apply the DFS framework to ABC, GABC and CABC, yielding DFSABC, DFSGABC and DFSCABC respectively. The experimental results on 22 benchmark functions show that the DFS framework can speed up convergence rate in most cases. To further improve the performance, we introduce two novel solution search equations: the first equation incorporates the information of elite solutions and can be applied to the employed bee phase, while the second equation not only exploits the information of the elite solutions but also employs the current best solution in the onlooker bee phase. Finally, two novel proposed search equations are combined with DFSABC to form a new variant of ABC, named DFSABC_elite. Through the comparison of DFSABC_elite with other variants of ABC and some non-ABC methods, the experimental results demonstrate that DFSABC_elite is significantly better than the compared algorithms on most of the test functions in terms of solution quality, robustness, and convergence speed.

Dynamic non-parametric joint sentiment topic mixture model

The reviews in social media are produced continuously by a large and uncontrolled number of users. To capture the mixture of sentiment and topics simultaneously in reviews is still a challenging task. In this paper, we present a novel probabilistic model framework based on the non-parametric hierarchical Dirichlet process (HDP) topic model, called non-parametric joint sentiment topic mixture model (NJST), which adds a sentiment level to the HDP topic model and detects sentiment and topics simultaneously from reviews. Then considered the dynamic nature of social media data, we propose dynamic NJST (dNJST) which adds time decay dependencies of historical epochs to the current epochs. Compared with the existing sentiment topic mixture models which are based on latent Dirichlet allocation (LDA), the biggest difference of NJST and dNJST is that they can determine topic number automatically. We implement NJST and dNJST with online variational inference algorithms, and incorporate the sentiment priors of words into NJST and dNJST with HowNet lexicon. The experiment results in some Chinese social media dataset show that dNJST can effectively detect and track dynamic sentiment and topics.

A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization

Abstract The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs) that is inspired by the collective foraging behavior of real honey bee colonies. Due to its foraging model and its solution search equation, ABC generally performs well in exploration but badly in exploitation. To address this concerning issue and obtain a good balance between exploration and exploitation, in this paper, we mainly introduce into the ABC an adaptive method for the population size (AMPS). AMPS is inspired by the natural principle that the size of a population is affected by the availability of food resources. When food resources are abundant, a population tends to expand; otherwise, a decrease in the availability of food resources leads to a shrinkage in the population size. Specifically, when the algorithm performs well in exploration, AMPS will shrink the population to enhance exploitation by periodically removing some inferior solutions that have low success rates. In contrast, AMPS will enlarge the population to improve exploration by introducing some reserved solutions. Furthermore, to make AMPS perform better, we design a new solution search equation for employed bees and onlooker bees. Moreover, we also improve the probability model of the onlooker bees. By embedding our three proposed algorithmic components into ABC, we propose a novel ABC variant, called APABC. To demonstrate the performance of APABC, we compare APABC with some state-of-the-art ABC variants and some other non-ABC methods on 22 scalable benchmark functions and 30 CEC2014 test functions. The simulation results show that APABC is better than or at least competitive with the competitors in terms of its solution quality, robustness and convergence speed.

A ranking-based adaptive artificial bee colony algorithm for global numerical optimization

Abstract The artificial bee colony (ABC) algorithm is a powerful population-based metaheuristic for global numerical optimization and has been shown to compete with other swarm-based algorithms. However, ABC suffers from a slow convergence speed. To address this issue, the natural phenomenon in which good individuals always have good genes and thus should have more opportunities to generate offspring is the inspiration for this paper. We propose a ranking-based adaptive ABC algorithm (ARABC). Specifically, in ARABC, food sources are selected by bees to search, and the parent food sources used in the solution search equation are all chosen based on their rankings. The higher a food source is ranked, the more opportunities it will have to be selected. Moreover, the selection probability of the food source is based on the corresponding ranking, which is adaptively adjusted according to the status of the population evolution. To evaluate the performance of ARABC, we compare ARABC with other ABC variants and state-of-the-art differential evolution and particle swarm optimization algorithms based on a number of benchmark functions. The experimental results show that ARABC is significantly better than the algorithms to which it was compared.

Dynamic Online HDP model for discovering evolutionary topics from Chinese social texts

User-generated content such as online reviews in social media evolve rapidly over time. To better understand the social media content, users not only want to examine what the topics are, but also want to discover the topic evolution patterns. In this paper, we propose a Dynamic Online Hierarchical Dirichlet Process model (DOHDP) to discover the evolutionary topics for Chinese social texts. In our DOHDP model, the evolutionary processes of topics are considered as evolutions in two levels, i.e. inter-epoch level and intra-epoch level. In inter-epoch level, the corpus of each epoch is modeled with an online HDP topic model, and the social texts are generated in a sequence mode. In the intra-epoch level, the time dependencies of historical epochs are modeled with an exponential decay function in which more recent epochs have a relatively stronger influence on the model parameters than the earlier epoch. Furthermore, we implement our DOHDP model using a two-phase online variational algorithm. Through comparing our DOHDP model with other related topic models on Chinese social media dataset Tianya-80299, the experiment results show that DOHDP model provides the best performance for discovering the evolutionary topics of Chinese social texts.

Combine HowNet lexicon to train phrase recursive autoencoder for sentence-level sentiment analysis

Abstract Detecting sentiment of sentences in online reviews is still a challenging task. Traditional machine learning methods often use bag-of-words representations which cannot properly capture complex linguistic phenomena in sentiment analysis. Recently, recursive autoencoder (RAE) methods have been proposed for sentence-level sentiment analysis. They use word embedding to represent each word, and learn compositional vector representation of phrases and sentences with recursive autoencoders. Although RAE methods outperform other state-of-the-art sentiment prediction approaches on commonly used datasets, they tend to generate very deep parse trees, and need a large amount of labeled data for each node during the process of learning compositional vector representations. Furthermore, RAE methods mainly combine adjacent words in sequence with a greedy strategy, which make capturing semantic relations between distant words difficult. To solve these issues, we propose a semi-supervised method which combines HowNet lexicon to train phrase recursive autoencoders (we call it CHL-PRAE). CHL-PRAE constructs the phrase recursive autoencoder (PRAE) model at first. Then the model calculates the sentiment orientation of each node with the HowNet lexicon, which acts as sentiment labels, when we train the softmax classifier of PRAE. Furthermore, our CHL-PRAE model conducts bidirectional training to capture global information. Compared with RAE and some supervised methods such as support vector machine (SVM) and naive Bayesian on English and Chinese datasets, the experiment results show that CHL-PRAE can provide the best performance for sentence-level sentiment analysis.

A hybrid multi-objective firefly algorithm for big data optimization

Abstract Multi-objective evolutionary algorithms (MOEAs) have shown good performance on many benchmark and real world multi-objective optimization problems. However, MOEAs may suffer from some difficulties when solving big data optimization problems with thousands of variables. Firefly algorithm (FA) is a new meta-heuristic, which has been proved to be a good optimization tool. In this paper, we present a hybrid multi-objective FA (HMOFA) for big data optimization. A set of big data optimization problems, including six single objective problems and six multi-objective problems, are tested in the experiments. Computational results show that HMOFA achieves promising performance on all test problems.