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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cl.2008.08.001 Byline: Zhi-jie Li (a)(b)(c), Chun-tian Cheng (b), Fei-xue Huang (d) Keywords: Resource allocation; Utility function; Computational grid; GridSim Abstract: Optimal resource allocation is a complex undertaking due to large-scale heterogeneity present in computational grid. Traditionally, the decision based on certain cost functions has been used in allocating grid resource as a standard method that does not take resource access cost into consideration. In this paper, the utility function is presented as a promising method for grid resource allocation. To tackle the issue of heterogeneous demand, the user's preference is represented by utility function, which is driven by a user-centric scheme rather than system-centric parameters adopted by cost functions. The goal of each grid user is to maximize its own utility under different constraints. In order to allocate a common resource to multiple bidding users, the optimal solution is achieved by searching the equilibrium point of resource price such that the total demand for a resource exactly equals the total amount available to generate a set of optimal user bids. The experiments run on a Java-based discrete-event grid simulation toolkit called GridSim are made to study characteristics of the utility-driven resource allocation strategy under different constraints. Results show that utility optimization under budget constraint outperforms deadline constraint in terms of time spent, whereas deadline constraint outperforms budget constraint in terms of cost spent. The conclusion indicates that the utility-driven method is a very potential candidate for the optimal resource allocation in computational grid. Author Affiliation: (a) Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, Liaoning, PR China (b) Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, Liaoning, PR China (c) Research Institute of Nonlinear Information Technology, Dalian Nationalities University, Dalian 116600, China (d) Department of Economics, Dalian University of Technology, Dalian 116024, Liaoning, PR China Article History: Received 10 August 2006; Revised 15 August 2008; Accepted 23 August 2008 Article Note: (footnote) [star] This work is supported by the National Natural Science Foundation of China under Grant no. 50479055.

Abstract: Dynamic programming (DP) is one of classic and sophisticated optimization methods that have successfully been applied to solve the problem of hydro unit load dispatch (HULD). However, DP will be faced with the curse of dimensionality with the increase of unit number and installed generating capacity of hydropower station. With the appearance of the huge hydropower station similar to the Three George with 26 generators of 700MW, it is hard to apply the DP to large scale HULD problem. It is crucial to seek for other optimization techniques in order to improve the operation quality and efficiency. Different with the most of literature about power generation scheduling that focused on the comparisons of novel PSO algorithms with other techniques, the paper will pay emphasis on comparison study of PSO with DP based on a case hydropower station. The objective of study is to seek for an effective and feasible method for the large scale of hydropower station of the current and future in China. This paper first compares the performance of PSO and DP using a sample load curve of the Wujiangdu hydropower plant located in the upper stream of the Yangtze River in China and contained five units with the installed capacity of 1250MW. Next, the effect of different load interval and unit number on the optimal results and efficiency of two methods has also been implemented. The comparison results show that the PSO is feasible for HULD. Furthermore, we simulated the effect of the magnitude of unit number and load capacity on the optimal results and cost time. The simulation comparisons show that PSO has a great advantage over DP in the efficiency and will be one of effective methods for HULD problem of huge hydropower stations. [Copyright &y& Elsevier]

To improve the efficiency of basic artificial immune algorithm ( AIA), this paper presents an improved artificial immune algorithm (IAIA) for multimodal function optimization. Appropriate cluster analysis was conducted on real values of individuals with Euclidean distance measure, then all individuals within a cluster were considered as a group with the same concentration value. The average entropy on the whole group Was calculated with binary encoding of individuals, which replaced the conventionally calculation of entropy between two individuals in AIA. To maintain high diversity, the fitness of each individual and concentration were taken into account in determining reproduction probability. Two benchmark functions were used to demonstrate the validity of IAIA and the role of each design of IAIA. Numerical experiments show that IAIA can reduce the complexity of computation, and then increases the efficiency of AIA with the maintenance of diversity and convergence in optimizing multimodal functions. [ABSTRACT FROM AUTHOR]

Abstract: Genetic Algorithm (GA) is globally oriented in searching and thus useful in optimizing multiobjective problems, especially where the objective functions are ill-defined. Conceptual rainfall–runoff models that aim at predicting streamflow from the knowledge of precipitation over a catchment have become a basic tool for flood forecasting. The parameter calibration of a conceptual model usually involves the multiple criteria for judging the performances of observed data. However, it is often difficult to derive all objective functions for the parameter calibration problem of a conceptual model. Thus, a new method to the multiple criteria parameter calibration problem, which combines GA with TOPSIS (technique for order performance by similarity to ideal solution) for Xinanjiang model, is presented. This study is an immediate further development of authors'' previous research (Cheng, C.T., Ou, C.P., Chau, K.W., 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. Journal of Hydrology, 268, 72–86), whose obvious disadvantages are to split the whole procedure into two parts and to become difficult to integrally grasp the best behaviors of model during the calibration procedure. The current method integrates the two parts of Xinanjiang rainfall–runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrated the intrinsic phenomenon of observed data in integrity. Comparison of results with two-step procedure shows that the current methodology gives similar results to the previous method, is also feasible and robust, but simpler and easier to apply in practice. [Copyright &y& Elsevier]

As an important renewable energy resource, hydropower plays an irreplaceable role in reducing power shortages in modern electrical systems throughout the world. This study focuses on the fixed head hydropower operation problem considering power shortages, which is classified as a large-scale nonlinear constrained optimization problem with a set of complex operational constraints. In order to efficiently solve this problem, a novel quadratic programming (QP) model is developed, in which the goal is to find the optimal hourly operational policy for all hydropower plants in a system so as to minimize the sum of squared energy deficits over the scheduling horizon. The results of a ten-reservoir hydropower system show that the proposed approach can efficiently find better solutions than several previous optimization methods for energy deficit reduction. Thus the proposed technique is effective for hydropower scheduling problems in which the assumption of fixed hydraulic head holds. [ABSTRACT FROM AUTHOR]