Aijia Ouyang

Hybrid particle swarm optimization for parameter estimation of Muskingum model

The Muskingum model is the most widely used and efficient method for flood routing in hydrologic engineering; however, the applications of this model still suffer from a lack of an efficient method for parameter estimation. Thus, in this paper, we present a hybrid particle swarm optimization (HPSO) to estimate the Muskingum model parameters by employing PSO hybridized with Nelder–Mead simplex method. The HPSO algorithm does not require initial values for each parameter, which helps to avoid the subjective estimation usually found in traditional estimation methods and to decrease the computation for global optimum search of the parameter values. We have carried out a set of simulation experiments to test the proposed model when applied to a Muskingum model, and we compared the results with eight superior methods. The results show that our scheme can improve the search accuracy and the convergence speed of Muskingum model for flood routing; that is, it has higher precision and faster convergence compared with other techniques.

ESTIMATING PARAMETERS OF MUSKINGUM MODEL USING AN ADAPTIVE HYBRID PSO ALGORITHM

In order to accelerate the convergence and improve the calculation accuracy for parameter optimization of the Muskingum model, we propose a novel, adaptive hybrid particle swarm optimization (AHPSO) algorithm. With the decreasing of inertial weight factor proposed, this method can gradually converge to a global optimal with elite individuals obtained by hybrid PSO. In the paper, we analyzed the feasibility and the advantages of the AHPSO algorithm. Then, we verified its efficiency and superiority by application of the Muskingum model. We intensively evaluated the error fitting degree based on the comparison with four known formulas: the test method (TM), the least residual square method (LRSM), the nonlinear programming method (NPM), and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The results show that the AHPSO has a higher precision. In addition, we compared the AHPSO algorithm with the binary-encoded genetic algorithm (BGA), the Gray genetic algorithm (GGA), the Gray-encoded accelerating genetic algorithm (GAGA) and the particle swarm optimization (PSO), and results show that AHPSO has faster convergent speed. Moreover, AHPSO has a competitive advantage compared with the above eight methods in terms of robustness. With the efficiency of this approach it can be extended to estimate parameters of other dynamic models.

A HYBRID CLUSTERING ALGORITHM COMBINING CLOUD MODEL IWO AND K-MEANS

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorith...

A Novel Hybrid Multi-Objective Population Migration Algorithm

This paper presents a multi-objective co-evolutionary population migration algorithm based on Good Point Set (GPSMCPMA) for multi-objective optimization problems (MOP) in view of the characteristics of MOPs. The algorithm introduces the theory of good point set (GPS) and dynamic mutation operator (DMO) and adopts the entire population co-evolutionary migration, based on the concept of Pareto nondomination and global best experience and guidance. The performance of the algorithm is tested through standard multi-objective functions. The experimental results show that the proposed algorithm performs much better in the convergence, diversity and solution distribution than SPEA2, NSGA-II, MOPSO and MOMASEA. It is a fast and robust multi-objective evolutionary algorithm (MOEA) and is applicable to other MOPs.

Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP

This paper first introduces the fundamental principles of immune algorithm (IA), greedy algorithm (GA) and delete-cross operator (DO). Based on these basic algorithms, a hybrid immune algorithm (HIA) is constructed to solve the traveling salesman problem (TSP). HIA employs GA to initialize the routes of TSP and utilizes DO to delete routes of crossover. With dynamic mutation operator (DMO) adopted to improve searching precision, this proposed algorithm can increase the likelihood of global optimum after the hybridization. Experimental results demonstrate that the HIA algorithm is able to yield a better solution than that of other algorithms, which also takes less computation time.

An Efficient Hybrid Algorithm Based on HS and SFLA

When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence preci...

Proactive workload management in dynamic virtualized environments

Recently, with the improvement of Cloud systems technologies and the essential advantages they can provide such as availability, scalability, and costs saving; massive domains in the IT industry are directing their business to the Cloud. To fit the computing demands of this trend along with nowadays fluky applications (e.g. social networks, media contents), Cloud systems require rapid resource changes. As a result, the workload management in a virtualized environment becomes a complex task. In this paper we propose a new proactive workload management model for virtualized resources to inspect the workload behavior of the running Virtual Machines, and to assent an appropriate scheduling and resource consolidation schema in order to improve the system efficiency, utilization, and throughput. We have carried out our model by modifying Xen Cloud Platform, then we tested the model performance through different representative benchmarks. The results show that the Proactive model can decrease the average response time remarkably.

GPU-Accelerated Parallel Hierarchical Extreme Learning Machine on Flink for Big Data

The extreme learning machine (ELM) has become one of the most important and popular algorithms of machine learning, because of its extremely fast training speed, good generalization, and universal approximation/classification capability. The proposal of hierarchical ELM (H-ELM) extends ELM from single hidden layer feedforward networks to multilayer perceptron, greatly strengthening the applicability of ELM. Generally speaking, during training H-ELM, large-scale datasets (DSTs) are needed. Therefore, how to make use of H-ELM framework in processing big data is worth further exploration. This paper proposes a parallel H-ELM algorithm based on Flink, which is one of the in-memory cluster computing platforms, and graphics processing units (GPUs). Several optimizations are adopted to improve the performance, such as cache-based scheme, reasonable partitioning strategy, memory mapping scheme for mapping specific Java virtual machine objects to buffers. Most importantly, our proposed framework for utilizing GPUs to accelerate Flink for big data is general. This framework can be utilized to accelerate many other variants of ELM and other machine learning algorithms. To the best of our knowledge, it is the first kind of library, which combines in-memory cluster computing with GPUs to parallelize H-ELM. The experimental results have demonstrated that our proposed GPU-accelerated parallel H-ELM named as GPH-ELM can efficiently process large-scale DSTs with good performance of speedup and scalability, leveraging the computing power of both CPUs and GPUs in the cluster.

Solving 0−1 knapsack problem by artificial chemical reaction optimization algorithm with a greedy strategy

This paper proposes a new artificial chemical reaction optimization algorithm with a greedy strategy to solve 0-1 knapsack problem. The artificial chemical reaction optimization (ACROA) inspiring the chemical reaction process is used to implement the local and global search. A new repair operator integrating a greedy strategy and random selection is used to repair the infeasible solutions. The experimental results have proven the superior performance of ACROA compared to genetic algorithm, and quantum-inspired evolutionary algorithm.

A parallel approximate SS-ELM algorithm based on MapReduce for large-scale datasets

Abstract Extreme Learning Machine (ELM) algorithm not only has gained much attention of many scholars and researchers, but also has been widely applied in recent years especially when dealing with big data because of its better generalization performance and learning speed. The proposal of SS-ELM (semi-supervised Extreme Learning Machine) extends ELM algorithm to the area of semi-supervised learning which is an important issue of machine learning on big data. However, the original SS-ELM algorithm needs to store the data in the memory before processing it, so that it could not handle large and web-scale data sets which are of frequent appearance in the era of big data. To solve this problem, this paper firstly proposes an efficient parallel SS-ELM (PSS-ELM) algorithm on MapReduce model, adopting a series of optimizations to improve its performance. Then, a parallel approximate SS-ELM Algorithm based on MapReduce (PASS-ELM) is proposed. PASS-ELM is based on the approximate adjacent similarity matrix (AASM) algorithm, which leverages the Locality-Sensitive Hashing (LSH) scheme to calculate the approximate adjacent similarity matrix, thus greatly reducing the complexity and occupied memory. The proposed AASM algorithm is general, because the calculation of the adjacent similarity matrix is the key operation in many other machine learning algorithms. The experimental results have demonstrated that the proposed PASS-ELM algorithm can efficiently process very large-scale data sets with a good performance, without significantly impacting the accuracy of the results.