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Abstract: In the past, research on multiple criteria scheduling assumes that the number of available machines is fixed during the whole scheduling horizon and research on scheduling with limited machine availability assumes that there is only a single criterion that needs to be optimized. Both assumptions may not hold in real life. In this paper we simultaneously consider both bicriteria scheduling and scheduling with limited machine availability. We focus on two parallel machines’ environment and the goal is to find preemptive schedules to optimize both makespan and total completion time subject to machine availability. Our main contribution in this paper is that we showed three bicriteria scheduling problems are in P by providing polynomial time optimal algorithms. [Copyright &y& Elsevier]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.sysarc.2010.05.014 Byline: Jonghee M. Youn (a), Minwook Ahn (a), Yunheung Paek (a), Jongwung Kim (b), Jeonghun Cho (b) Keywords: Embedded processor; Compiler; Optimization; Addressing mode Abstract: The complexity of today's embedded applications increases with various requirements such as execution time, code size or power consumption. To satisfy these requirements for performance, efficient instruction set design is one of the important issues because an instruction customized for specific applications can make better performance than multiple instructions in aspect of fast execution time, decrease of code size, and low power consumption. Limited encoding space, however, does not allow adding application specific and complex instructions freely to the instruction set architecture. To resolve this problem, conventional architectures increases free space for encoding by trimming excessive bits required beyond the fixed word length. This approach however shows severe weakness in terms of the complexity of compiler, code size and execution time. In this paper, we propose a new instruction encoding scheme based on the dynamic implied addressing mode (DIAM) to resolve limited encoding space and side-effect by trimming. We report our two versions of architectures to support our DIAM-based approach. In the first version, we use a special on-chip memory to store extra encoding information. In the second version, we replace the memory by a small on-chip buffer along with a special instruction. We also suggest a code generation algorithm to fully utilize DIAM. In our experiment, the architecture augmented with DIAM shows about 8% code size reduction and 18% speed up on average, as compared to the basic architecture without DIAM. Author Affiliation: (a) School of Electrical Engineering and Computer Science, Seoul National University, Republic of Korea (b) School of Electrical Engineering and Computer Science, Kyungpook National University, Republic of Korea Article History: Received 1 October 2009; Revised 14 May 2010; Accepted 31 May 2010 Article Note: (footnote) [star] This is a revised and expanded version of two papers published in the 7th IEEE Symposium on Application Specific Processors, July 2009 and the 11th IEEE International Conference on High Performance Computing and Communications, June 2009 .

Abstract: Finding shortest paths is a fundamental problem in graph theory, which has a large amount of applications in many areas like computer science, operations research, network routing and network analysis. Although many exact and approximate algorithms have been proposed, it is still a time-consuming task to calculate shortest paths for large-scale networks with tremendous volume of data available in recent years. In this paper, we find that the classic Dijkstra''s algorithm can be improved by simple modification. We propose a fast algorithm which utilize the preiously-calculated results to accelerate the latter calculation. Simple optimization strategies are also proposed with consideration of characteristics of scale-free complex networks. Our experimental results show that the average running time of our algorithm is lower than the Dijkstra''s algorithm by a factor relating to the connection probability in random networks of ER model. The performance of our algorithm is significantly better than the Dijkstra''s algorithm in scale-free networks generated by the AB model. The results show that the time complexity is reduced to about O(n2.4) in scalefree complex networks. When the optimization strategies are applied, the algorithm performance is further improved slightly in scale-free networks. [Copyright &y& Elsevier]

Abstract: Hybrid strategy, which generalizes a specific single-label algorithm while one or two data decomposition tricks are applied implicitly or explicitly, has become an effective and efficient tool to design and implement various multi-label classification algorithms. In this paper, we extend traditional binary support vector machine by introducing an approximate ranking loss as its empirical loss term to build a novel support vector machine for multi-label classification, resulting into a quadratic programming problem with different upper bounds of variables to characterize label correlation of individual instance. Further, our optimization problem can be solved via combining one-versus-rest data decomposition trick with modified binary support vector machine, which dramatically reduces computational cost. Experimental study on ten multi-label data sets illustrates that our method is a powerful candidate for multi-label classification, compared with four state-of-the-art multi-label classification approaches. [Copyright &y& Elsevier]

Abstract: Last few years have witnessed the development of various multi-objective evolutionary algorithms since they allow the generation of the overall Pareto front for multi-objective optimizations. With the problems in the real-world becoming more and more complex, however, no reported work in the literature focuses on the high-dimensional objective optimizations (HOPs). In this paper, we propose an evolutionary algorithm named HOEA (high-dimensional objective evolutionary algorithm) for HOPs. By adopting the concept of nonlinear definition for optimizing object, HOPs can be solved by HOEA, while the well-known multi-objective evolutionary algorithms work poorly on HOPs. We further analyze the nonlinear dynamic properties of HOEA on the basis of martingale theoretical framework. The theoretical results indicate that this new algorithm is indeed capable of achieving convergence. We also conduct experiments on HOEA with two representative test instances. The experimental results either confirm our theoretical results or show that the proposed algorithm is efficient and effective for HOPs. [Copyright &y& Elsevier]

Abstract: The support vector clustering (SVC) algorithm consists of two main phases: SVC training and cluster assignment. The former requires calculating Lagrange multipliers and the latter requires calculating adjacency matrix, which may cause a high computational burden for cluster analysis. To overcome these difficulties, in this paper, we present an improved SVC algorithm. In SVC training phase, an entropy-based algorithm for the problem of calculating Lagrange multipliers is proposed by means of Lagrangian duality and the Jaynes’ maximum entropy principle, which evidently reduces the time of calculating Lagrange multipliers. In cluster assignment phase, the kernel matrix is used to preliminarily classify the data points before calculating adjacency matrix, which effectively reduces the computing scale of adjacency matrix. As a result, a lot of computational savings can be achieved in the improved algorithm by exploiting the special structure in SVC problem. Validity and performance of the proposed algorithm are demonstrated by numerical experiments. [Copyright &y& Elsevier]

Abstract: The differential evolution (DE) is a global optimization algorithm to solve numerical optimization problems. Recently the quantum-inquired differential evolution (QDE) has been proposed for binary optimization. This paper proposes DE/QDE to learn the Takagi–Sugeno (T–S) fuzzy model. DE/QDE can simultaneously optimize the structure and the parameters of the model. Moreover a new encoding scheme is given to allow DE/QDE to be easily performed. The two benchmark problems are used to validate the performance of DE/QDE. Compared to some existing methods, DE/QDE shows the competitive performance in terms of accuracy. [Copyright &y& Elsevier]

Abstract: This paper discusses optimization of quality measures over first order Delaunay triangulations. Unlike most previous work, our measures relate to edge-adjacent or vertex-adjacent triangles instead of only to single triangles. We give efficient algorithms to optimize certain measures, including measures related to the area ratio of adjacent triangles, angle between outward normals of adjacent triangles (for polyhedral terrains), and number of convex vertices. Some other measures are shown to be NP-hard. These include maximum vertex degree, number of convex edges, and number of mixed vertices. For the latter two measures we provide, for any constant , factor approximation algorithms that run in and time (when the Delaunay triangulation is given). For minimizing the maximum vertex degree, the NP-hardness proof provides an inapproximability result. Our results are presented for the class of first order Delaunay triangulations, but also apply to triangulations where for every triangle at least two edges are fixed. The approximation result on maximizing the number of convex edges is also extended to k-th order Delaunay triangulations. [Copyright &y& Elsevier]

Abstract: Trade-off (aka Pareto) curves are typically used to represent the trade-off among different objectives in multiobjective optimization problems. Although trade-off curves are exponentially large for typical combinatorial optimization problems (and infinite for continuous problems), it was observed in Papadimitriou and Yannakakis [On the approximability of trade-offs and optimal access of web sources, in: Proc. 41st IEEE Symp. on Foundations of Computer Science, 2000] that there exist polynomial size approximations for any , and that under certain general conditions, such approximate -Pareto curves can be constructed in polynomial time. In this paper we seek general-purpose algorithms for the efficient approximation of trade-off curves using as few points as possible. In the case of two objectives, we present a general algorithm that efficiently computes an -Pareto curve that uses at most 3 times the number of points of the smallest such curve; we show that no algorithm can be better than 3-competitive in this setting. If we relax to any , then we can efficiently construct an -curve that uses no more points than the smallest -curve. With three objectives we show that no algorithm can be c-competitive for any constant c unless it is allowed to use a larger value. We present an algorithm that is 4-competitive for any . We explore the problem in high dimensions and give hardness proofs showing that (unless P=NP) no constant approximation factor can be achieved efficiently even if we relax by an arbitrary constant. [Copyright &y& Elsevier]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2009.06.008 Byline: Jihyun Lee (a), Jeong-Hoon Park (a), Myung-Jae Park (a), Chin-Wan Chung (a), Jun-Ki Min (b) Keywords: Distributed query processing; Semantic mapping; Query optimization; Distributed ontologies; Semantic Web Abstract: In this paper, we propose an intelligent distributed query processing method considering the characteristics of a distributed ontology environment. We suggest more general models of the distributed ontology query and the semantic mapping among distributed ontologies compared with the previous works. Our approach rewrites a distributed ontology query into multiple distributed ontology queries using the semantic mapping, and we can obtain the integrated answer through the execution of these queries. Furthermore, we propose a distributed ontology query processing algorithm with several query optimization techniques: pruning rules to remove unnecessary queries, a cost model considering site load balancing and caching, and a heuristic strategy for scheduling plans to be executed at a local site. Finally, experimental results show that our optimization techniques are effective to reduce the response time. Author Affiliation: (a) Division of Computer Science, Department of Electronic Engineering and Computer Science, Korea Advanced Institute of Science and Technology (KAIST), Daejon 305-701, Republic of Korea (b) School of Internet-Media Engineering, Korea University of Technology and Education, Byeongcheon-Myeon, Cheonan, Chungnam 330-708, Republic of Korea Article History: Received 30 October 2008; Revised 30 March 2009; Accepted 7 June 2009