Xingquan Zuo

Deadline Constrained Task Scheduling Based on Standard-PSO in a Hybrid Cloud

Public cloud providers provide Infrastructure as a Service (IaaS) to remote users. For IaaS providers, how to schedule tasks to meet peak demand is a big challenge. Previous researches proposed purchasing machines in advance or building cloud federation to resolve this problem. However, the former is not economic and the latter is hard to be put into practice at present. In this paper, we propose a hybrid cloud architecture, in which an IaaS provider can outsource its tasks to External Clouds (ECs) without establishing any agreement or standard when its local resources are not sufficient. The key issue is how to allocate users’ tasks to maximize its profit while guarantee QoS. The problem is formulated as a Deadline Constrained Task Scheduling (DCTS) problem which is resolved by standard particle swarm optimization (PSO), and compared with an exact approach (CPLEX). Experiment results show that Standard-PSO is very effective for this problem.

New modified bare-bones particle swarm optimization

Bare-bones Particle Swarm Optimization (BPSO) is a simplified PSO variant, which has shown potential performance on many multimodal optimization problems. However, BPSO is also possible to be trapped into local optima for high-dimensional and complicated optimization problems. In order to enhance the performance of BPSO, this paper presents a modified BPSO, called NMBPSO. It combined the ideas of the traditional PSO and a modified BPSO to improve the capacity of balancing exploration and exploitation during the search process. To verify the effect and benefit of the proposed algorithm, a set of well known benchmark functions are employed and compared against some competitive PSO variants. Experiment results indicate that NMBPSO performs better than the traditional PSO, BPSO and a modified BPSO algorithm.

A PSO- based robust optimization approach for supply chain collaboration with demand uncertain

A robust optimization approach is proposed to solve the problem of supply chain collaboration under a demand uncertain environment. The proposed approach is universal and able to adapt to various demand models. First, the uncertain demand is described by a set of sampling sequences, and the total cost of supply chain is calculated based on these sequences to evaluate a collaboration scheme. Then a particle swarm optimization (PSO) is employed to find the optimal collaboration scheme which leads to a minimum total cost of supply chain. Numerical experiments show that the proposed approach can produce a robust solution that is insensible to the effect of demand uncertainty.

Conservatism and Adventurism in Particle Swarm Optimization Algorithm.

Particle Swarm Optimization (PSO) is a widely used optimization algorithm in industrial and academic fields. In this paper, three improved PSO variants are proposed. The main ideas of them are that a coefficient v is added to control the velocity augment of particles to the new position on different dimension. The first one is under the guidance of conservatism which is an inspiration of Differential Evolution (DE), namely, particles preserve more information from their previous positions and move in a smaller search space. This algorithm shows that particles are possible to escape from the current neighborhood and for promising search area if they take more previous information. The second one is guided by adventurism for better exploration, which means a larger search space to particles. The third one can be considered as a compromise between conservatism and adventurism. This algorithm shows that a balanced cooperation with a little conservative in more adventures will make PSO more competitive. Experimental results show that the proposed strategies of all the three algorithms are effective based on CEC2015 benchmarks. All of them are better than the traditional PSOs and the third improved variant performs better than all the other competitors.

Elite-Leading Fireworks Algorithm

Fireworks algorithm (FWA) is effective to solve optimization problems as a swarm intelligence algorithm. In this paper, the elite-leading fireworks algorithm (ELFWA) is proposed based on dynamic search in fireworks algorithm (dynFWA), which is an important improvement of FWA. In dynFWA firework is separated to two group: core-firework (CF) and non-core fireworks (non-CFs). This paper takes some beneficial information from non-CFs to reinforce the local search effect of CF. Random reinitialization and elite-leading operator are used to maintain the diversity of the non-CFs, which play an important role in global search. Based on the CEC2015 benchmark functions suite, ELFWA has a very competitive performance when comparing with state-of-the-art fireworks algorithms, such as dynFWA, dynFWACM and eddynFWA.

A Recommendation Approach Based on Latent Factors Prediction of Recurrent Neural Network

Recommender systems have received much attention due to their wide applications. Current recommender approaches typically recommend items to user based on the rating prediction. However, the predicted ratings cannot truly reflect users interests on items because the rating prediction is usually based on history data and does not consider the effect of time factor on uses interests (behaviors). In this paper, we propose a recommendation approach combining the matrix factorization and a recurrent neural network. In this approach, all the items rated by a user are considered as time series data. The matrix factorization is used to obtain latent vectors of those items. The recurrent neural network is taken as a time series prediction model and trained by the latent vectors of historical items, and then the trained model is used to predict the latent vector of the item to be recommended. Finally, a recommendation list is formed by mapping the latent vector into a set of items. Experimental results show that the proposed approach is able to produce an effective recommend list and outperforms those comparative approaches.

Semi-self-adaptive harmony search algorithm

This paper proposes a semi-self-adaptive harmony search algorithm (SSaHS) with the self-adaptive adjustment of the bandwidth and the elitist learning strategy of particle swarm optimization. SSaHS employs a self-adaptive adjusting strategy for with the difference between the maximum and minimum components in the harmony memory as the bandwidth. It can dynamically adjust the bandwidth for the specific problem to strengthen local exploitation ability and improve the accuracy of optimization results. Comparison results show that the semi-self-adaptive harmony search algorithm can find better solutions when comparing with both basic harmony search algorithm and several enhanced harmony search algorithms, including an improved harmony search, a global-best harmony search and a novel global harmony search.

Predicting Virtual Machine’s Power via a RBF Neural Network

Data centers are growing rapidly in recent years. Data centers consume a huge amount of power, therefore how to save power is a key issue. Accurately predicting the power of virtual machine (VM) is significant to schedule VMs in different physical machines (PMs) to save power. Current researches rarely consider the impact of workload on this prediction. This paper studies the power prediction of VM under the multi-VM environment, with consideration of the impact of PMs’ workload. A RBF neural network approach is proposed to predict the VM’s power. Experiments show that the proposed approach is effective for VM’s power prediction and can achieve average error less than 2 %, which is smaller than those of comparative models.

Differential Evolution Algorithm with the Second Order Difference Vector

DE is challenging to maintain a balance between exploration and exploitation behaviors, and also the neighborhood and direction information of the difference vector is not completely utilized. In this paper, a completely novel DE variant, SODE, is proposed with the second order difference information, which is introduced to DE for even more fully utilizing the heuristic direction information. The second order difference information also enriches the neighborhood structure and enlarges the neighborhood domain with more heuristic information. Preliminary experimental results show that SODE is better than, or at least comparable to, the classical first order DE algorithms in terms of convergence performance and accuracy.

A Very Fast Convergent Evolutionary Algorithm for Satisfactory Solutions

As we know, genetic algorithm converges slowly. It is a natural contradiction when the situation appears with expensive objective function evaluating and satisfactory solutions being adequate. In this paper, a very fast convergent evolutionary algorithm (VFEA) is proposed with inner-outer hypercone crossover, problem dependent and search status involved mutation (PdSiMu). The offsprings produced by hypercone crossover are allowed to be outside the hypercone generated by rotating the parents around their bisectrix. PdSiMu utilizes the problem and evolving information quickly. VFEA is experimentally compared with five competitors based on ten classic 30 dimensional benchmarks. Experimental results indicate that VFEA can reach the accuracy of 10− 4 − 10− 1 for all the benchmarks within 1500 function evaluations. VFEA arrives significantly better performance than all its competitors with higher solution accuracy and stronger robustness.