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Zhang W.-X.,Sun Yat Sen University | Chen W.-N.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhang J.,Key Laboratory of Machine Intelligence and Advanced Computing
Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016 | Year: 2016

In this paper, a dynamic competitive swarm optimizer (DCSO) based on population entropy is proposed. The new algorithm is derived from the competitive swarm optimizer (CSO). The new algorithm uses population entropy to make a quantitative description about the diversity of population, and to divide the population into two sub-groups dynamically. During the early stage of the execution process, to speed up convergence of the algorithm, the sub-group with better fitness will have a small size, and worse large sub-group will learn from small one. During the late stage of the execution process, to keep the diversity of the population, the sub-group with better fitness will have a large size, and small worse sub-group will learn from large group. The proposed DCSO is evaluated on CEC'08 benchmark functions on large scale global optimization. The simulation results of the example indicate that the new algorithm has better and faster convergence speed than CSO. © 2016 IEEE. Source

Yu Z.,South China University of Technology | Li L.,Chinese University of Hong Kong | Liu J.,Hong Kong Baptist University | Zhang J.,Sun Yat Sen University | And 2 more authors.
IEEE Transactions on Knowledge and Data Engineering | Year: 2015

Cluster ensemble is one of the main branches in the ensemble learning area which is an important research focus in recent years. The objective of cluster ensemble is to combine multiple clustering solutions in a suitable way to improve the quality of the clustering result. In this paper, we design a new noise immune cluster ensemble framework named as AP2CE to tackle the challenges raised by noisy datasets. AP2CE not only takes advantage of the affinity propagation algorithm (AP) and the normalized cut algorithm (Ncut), but also possesses the characteristics of cluster ensemble. Compared with traditional cluster ensemble approaches, AP2CE is characterized by several properties. (1) It adopts multiple distance functions instead of a single Euclidean distance function to avoid the noise related to the distance function. (2) AP2CE applies AP to prune noisy attributes and generate a set of new datasets in the subspaces consists of representative attributes obtained by AP. (3) It avoids the explicit specification of the number of clusters. (4) AP2CE adopts the normalized cut algorithm as the consensus function to partition the consensus matrix and obtain the final result. In order to improve the performance of AP2CE, the adaptive AP2CE is designed, which makes use of an adaptive process to optimize a newly designed objective function. The experiments on both synthetic and real datasets show that (1) AP2CE works well on most of the datasets, in particular the noisy datasets; (2) AP2CE is a better choice for most of the datasets when compared with other cluster ensemble approaches; (3) AP2CE has the capability to provide more accurate, stable and robust results. © 2015 IEEE. Source

Yu W.-J.,Sun Yat Sen University | Yu W.-J.,Key Laboratory of Machine Intelligence and Advanced Computing | Li J.-J.,South China Normal University | Zhang J.,Sun Yat Sen University | Wan M.,Center for Science and Technology Development
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference | Year: 2014

Differential evolution (DE) has been demonstrated to be one of the most promising evolutionary algorithms (EAs) for global numerical optimization. DE mainly differs from other EAs in that it employs difference of the parameter vectors in mutation operator to search the objective function landscape. Therefore, the performance of a DE algorithm largely depends on the design of its mutation strategy. In this paper, we propose a new kind of DE mutation strategies whose greediness degree can be adaptively adjusted. The proposed mutation strategies utilize the information of top t solutions in the current population. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the degree of greediness to fit for different optimization scenarios, the parameter t is adjusted in each generation of the algorithm by an adaptive control scheme. This way, the convergence performance and the robustness of the algorithm can be enhanced at the same time. To evaluate the effectiveness of the proposed adaptive greedy mutation strategies, the approach is applied to original DE algorithms, as well as DE algorithms with parameter adaptation. Experimental results indicate that the proposed adaptive greedy mutation strategies yield significant performance improvement for most of cases studied. © 2014 ACM. Source

Liu X.-F.,Sun Yat Sen University | Liu X.-F.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Machine Intelligence and Advanced Computing | And 2 more authors.
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference | Year: 2014

Cloud computing provides resources as services in pay-as-yougo mode to customers by using virtualization technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in data center. More physical servers means more energy consumption and more money cost. Therefore, the VM placement (VMP) problem is significant in cloud computing. This paper proposes an approach based on ant colony optimization (ACO) to solve the VMP problem, named as ACO-VMP, so as to effectively use the physical resources and to reduce the number of running physical servers. The number of physical servers is the same as the number of the VMs at the beginning. Then the ACO approach tries to reduce the physical server one by one. We evaluate the performance of the proposed ACO-VMP approach in solving VMP with the number of VMs being up to 600. Experimental results compared with the ones obtained by the first-fit decreasing (FFD) algorithm show that ACO-VMP can solve VMP more efficiently to reduce the number of physical servers significantly, especially when the number of VMs is large. © 2014 ACM. Source

Zhan Z.-H.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhang J.,Key Laboratory of Software Technology
TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence | Year: 2015

Power electronic circuit (PEC) design and optimization is a significant problem in both scientific and engineering communities. Due to the complex search space of the PEC optimization problem, lots of works have tried to use evolutionary computation (EC) algorithms to solve it, and have gained great progress. However, some existing EC based algorithms for PEC are still complex in algorithm design, or the solutions are still needed to be improved when considering the solution accuracy. Therefore, design a simpler yet powerful algorithm to solve the PEC problem efficiently is in great need. This paper makes the first attempt to proposing a novel differential evolution (DE), which is a kind of new, simple, yet efficient EC algorithm for the PEC design and optimization. The advantage of this paper is that the DE algorithm is the first time directly applied to PEC design and optimization, making the approach very simple for use. The results are compared with those obtained by using genetic algorithm (GA), particle swarm optimization (PSO), and brain storm optimization (BSO). Results show that the DE algorithm outperforms GA, PSO, and BSO in our PEC design and optimization study. © 2015 IEEE. Source

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