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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Peng, Meng-Qi Gong, Yue-Jiao Li, Jing-Jing Lin, Ying-Biao Department of Computer Science Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Key Laboratory of Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China

ISBN: (纸本)9781450328814

Inspired by the division of labor and migration behavior in nature, this paper proposes a novel particle swarm optimization algorithm with multiple learning strategies (PSO-MLS). In the algorithm, particles are divided into three sub-swarms randomly while three learning strategies with different motivations are applied to each sub-swarm respectively. The Traditional Learning Strategy (TLS) inherits the basic operations of PSO to guarantee the stability. Then a Periodically Stochastic Learning Strategy (PSLS) employs a random learning vector to increase the diversity so as to enhance the global search ability. A Random Mutation Learning Strategy (RMLS) adopts mutation to enable particles to jump out of local optima when trapped. Besides, information migration is applied within the intercommunication of sub-swarms. After a certain number of generations, sub-swarms would aggregate to continue search, aiming at global convergence. Through these learning strategies and swarm aggregation, PSO-MLS possesses both good exploration and exploitation abilities. PSO-MLS was tested on a set of benchmarks and the result shows its superiority to gain higher accuracy for unimodal functions and better solution quality for multimodal functions when compared to some PSO variants.

关键词： Stochastic systems

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Evolutionary computation for lifetime maximization of wireless sensor networks in complex 3D environments 14

Evolutionary computation for lifetime maximization of wirele...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Zhang, Xin-Yuan Gong, Yue-Jiao Li, Jing-Jing Lin, Ying Department of Computer Science Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Key Laboratory of Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China Department of Psychology Sun Yat-sen University China

ISBN: (纸本)9781450328814

Scheduling the operating mode of nodes is an effective way to maximize the lifetime of wireless sensor networks (WSN). For a WSN with randomly and densely deployed sensors, we could maximize the lifetime of WSN through finding the maximum number of disjoint complete cover sets. Most of the related work focuses on 2D ideal plane. However, deploying sensors on the 3D surface is more practical in real world scenarios. We propose a novel genetic algorithm with redundant sensor auto-adjustment, termed RSAGA. In order to adapt the original GA into this application, we employ some effective mechanisms along with the basic crossover, mutation, and selection operation. The proposed operator of redundant sensor auto-adjustment schedules the redundant sensors in complete cover sets into incomplete cover sets so as to improve the coverage of the latters. A rearrangement operation specially designed for the critical sensors is embedded in the mutation operator to fine-tune the node arrangement of critical fields. Moreover, we modify the traditional cost function by increasing the penalty of incomplete cover sets for improving the convergence rate of finding feasible solutions. Simulation has been conducted to evaluate the performance of RSAGA. The experimental results show that the proposed RSAGA possesses very promising performance in terms of solution quality and robustness.

关键词： Genetic algorithms

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Combining multipopulation evolutionary algorithms with memory for dynamic optimization problems

Combining multipopulation evolutionary algorithms with memor...

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Congress on Evolutionary Computation

作者： Tao Zhu Wenjian Luo Lihua Yue Anhui Province Key Laboratory of Software Engineering in Computing and Communication Hefei Anhui China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781479914869

Both multipopulation and memory are widely used approaches in the field of evolutionary dynamic optimization. It would be interesting to examine the effect of the combinations of multipopulation algorithms (MPAs) and memory schemes. However, since most of the existing memory schemes are proposed with single population algorithms, straightforwardly applying them to MPAs may cause problems. By addressing the possible problems, a new memory scheme is proposed for MPAs in this paper. In the experiments, several existing memory schemes and the newly proposed scheme are combined with a MPA, i.e. the Species-based Particle Swarm Optimizer (SPSO), and these combinations are tested on cyclic and acyclic problems. The experimental results indicate that 1) straightforwardly using the existing memory schemes sometimes degrades the performance of SPSO even on cyclic problems; 2) the newly proposed memory scheme is very competitive.

关键词： Sociology Statistics Memory management Optimization Heuristic algorithms Benchmark testing Particle swarm optimization

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A novel genetic algorithm based on partitioning for large-scale network design problems 14

A novel genetic algorithm based on partitioning for large-sc...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Huang, Xiao-Ma Gong, Yue-Jiao Li, Jing-Jing Hu, Xiao-Min Department of Computer Science Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Key Laboratory of Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China School of Public Health Sun Yat-sen University China

ISBN: (纸本)9781450328814

Network design is a broad class of essential engineering and science problems. The target of network design is to construct a graph that satisfies some restrictions. Many network design problems (NDPs) are known as NP-hard and become more challenging as networks grow fast in size. In this paper, we propose a novel genetic algorithm based on partitioning, termed PGA, which divides large-scale NDPs into low dimensional subproblems and achieves global optimal solution by coordination of sub-problems. Experiments with PGA applied to the degreeconstrained minimum spanning tree problem have shown the effectiveness of PGA for large-scale NDPs.

关键词： Genetic algorithms

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Research on cloud computing complex adaptive agent

Research on cloud computing complex adaptive agent

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International Conference on Services Systems and Services Management, ICSSSM

作者： Qingyi Chen Hongwei Kang Hua Zhou Xingping Sun Yong Shen YunZhi Jin Jun Yin Cloud computing research center of Yunnan China School of Software Yunnan University Kunming Yunnan China Key Laboratory for Software Engineering of Yunna Province Republic of China

It has gradually realized in the industry that the increasing complexity of cloud computing under interaction of technology, business, society and the like, instead of being simply solved depending on research on information technology, shall be explained and researched from a systematic and scientific perspective on the basis of theory and method of a complex adaptive system (CAS). This article, for basic problems in CAS theoretical framework, makes research on definition of an active adaptive agent constituting the cloud computing system, and proposes a service agent concept and basic model through commonality abstraction from two basic levels: cloud computing technology and business, thus laying a foundation for further development of cloud computing complexity research as well as for multi-agent based cloud computing environment simulation.

关键词： Cloud computing Computational modeling Adaptive systems Complexity theory Business Adaptation models Economics

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IPRAR: A DFG-based approach to instruction clustering for multicluster VLIW DSP processor with SIMD structure

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Journal of Computational Information Systems 2014年第3期10卷 1257-1269页

作者： Yang, Yangzhao Gu, Naijie Zhao, Zeng Ren, Kaixin School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Computing and Communication Software Hefei 230027 China USTC and Loongson Technology Associated Laboratory Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China

Modern Very Long Instruction (VLIW) DSP can improve performance by using instruction-level parallelism (ILP) and obtain better parallelism through instruction clustering. The essence of clustering is resource allocation. Traditional clustering method assumes that one instruction can only be assigned to a certain cluster, which is inapplicable to the architecture with SIMD structure. To address this issue, this paper presents IPRAR, a novel instruction clustering algorithm for the features of SIMD structure based on DFG. Due to considering the features of SIMD structure, IPRAR can be implemented on BWDSP100 with satisfactory. Besides, IPRAR can make the load of cluster more balanced and minimize the intercluster communication overload. Finally, numerous experiment results show that the IPRAR has an average of 7-10% performance improvement compared to the traditional methods. © 2014 Binary Information Press.

关键词： Clustering algorithms

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Optimizing BLAS2 for a decoupled access/execute architecture processor

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Journal of Computational Information Systems 2014年第3期10卷 1231-1241页

作者： Zhao, Zeng Gu, Naijie Yang, Yangzhao Ren, Kaixin School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China USTC and Loongson Technology Associated Laboratory Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Computing and Communication Software Hefei 230027 China

Basic Linear Algebra Subroutines (BLAS) is a widely used basic mathematical library for high-performance computing. It has a great impact on the performance of supercomputers. This paper focuses on memory accessing optimization for the level-2 library of BLAS (BLAS2) based on Godson-3B chips. The key contribution of this work is proposing several methods to make full use of the Godson-3B's hardware features, as well as to improve the performance of memory access. GEMV, the kernel function of BLAS2, is selected to demonstrate the optimization methods for Godson-3B. Using Single Instruction Multiple Data (SIMD) instructions, Direct Register Access (DRA) and Direct Cache Access (DCA) of Godson-3B, two optimization algorithms for GEMV are introduced in this paper. Experiments show that the best computing performance of the optimized GEMVs on Godson-3B is more than 1.8Gops, which is far superior to that of other versions. While the test scale of GEMVs is greater than the size of L2-cache, its average performance exceeds UBLAS and OpenBlas, the two most famous optimized BLAS libraries for GODSON, by more than 60% and 20% respectively. © 2014 Binary Information Press.

关键词： Supercomputers

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FACIAL ACTION UNIT RECOGNITION BY RELATION MODELING FROM BOTH QUALITATIVE KNOWLEDGE AND QUANTITATIVE DATA

FACIAL ACTION UNIT RECOGNITION BY RELATION MODELING FROM BOT...

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IEEE International Conference on Multimedia and Expo Workshops

作者： Zhen Gao Shangfei Wang Chongliang Wu Jun Wang Qiang Ji Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

ISBN: (纸本)9781479947164

In this paper, we propose to capture Action Unit (AU) relations existing in both qualitative knowledge and quantitative data through Credal Networks (CN). Each node of the CN represents an AU label, and the links and probability intervals capture the probabilistic dependencies among multiple AUs. The structure of CN is designed based on prior knowledge. The parameters of CN are learned from both knowledge and ground-truth AU labels. The AU preliminary estimations are obtained by an existing image-driven recognition method. With the learned credal network, we infer the true AU labels by combining the relationships among labels with the previous obtained estimations. Experimental results on the CK+ database and MMI database demonstrate that with complete AU labels, our CN model is slightly better than the Bayesian Network (BN) model, demonstrating that credal sets learned from data can capture uncertainty more reliably;With incomplete and error-prone AU annotations, our CN model outperforms the BN model, indicating that credal sets can successfully capture qualitative knowledge.

关键词： Prior knowledge Credal network AU recognition

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Classifying and clustering in negative databases

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Frontiers of Computer Science 2013年第6期7卷 864-874页

作者： Ran LIU Wenjian LUO Lihua YUE School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 China

Recently, negative databases （NDBs） are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor （kNN） classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novel method for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.

关键词： negative databases classification clustering k nearest neighbor k-means hamming distance

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Simultaneous measurement of refractive index, temperature and strain based on core diameter mismatch and polarization-maintaining FBG

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Optoelectronics Letters 2013年第3期9卷 238-240页

作者：童峥嵘王洁玉张卫华曹晔 Key Laboratory of Film Electronics and Communication Devices and Key Laboratory of Intelligent Computing and Novel Software Technology School of Computer and Communication Engineering Tianjin University of Technology

A kind of fiber-optic sensor for simultaneous measurement of refractive index of surrounding medium, temperature and strain is described. Based on core diameter mismatch, a multimode-single mode-multimode (MSM) structure is presented. It is demonstrated that the three parameters can be measured respectively by the interference of the core mode and cladding modes excited in the single mode fiber (SMF). Then combined with a polarization-maintaining fiber Bragg grating (PMFBG) which has different sensing properties from MSM structure, three parameters are measured simultaneously. The experimental results show that PMFBG is insensitive to the refractive index and the refractive index sensitivity of the MSM structure is 96.04 nm·RIU^(-1); the temperature sensitivities of the characteristic wavelength for MSM structure and the center wavelengths of fast and slow axes for PMFBG are 0.0911 nm °C^(-1), 0.00976 nm °C^(-1) and 0.0105 nm °C^(-1), respectively; the strain sensitivities of those are -0.013 nm με^(-1), 0.012 nm με^(-1) and 0.012 nm με^(-1), respectively.

关键词：温度敏感性应变灵敏度同时测量折射率不匹配光纤光栅直径 MSM结构

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