Recently, the application of Ant ColonyOptimization is much wider and it is always the highlight of key algorithm. There are also many improvements about the ACO algorithm, such as: the improvements of algorithm in self-adaptive, the improvements of increasing the diversity of various group, the improvements of enhancing local search, combining with the global optimization algorithm, and combining with deterministic local optimization algorithm, etc..However, with the developments of the theory and technology in multicore computing, how to implement ACO efficiently and parallel in a multicore computing environment being a new challenge for all researchers. This paper will propose a new ACO algorithm based on the multicore computing environment.The ACO is the probability algorithm used for searching optimization paths. It was proposed by Marco Dorigo in his doctoral dissertation in 1992, and the idea was from the activities that ants explore ways when they are looking for food. ACO is a kind of simulated evolutionary algorithm, and it has many advantages based on the previous research. Point to the issue of the parameter optimized design in PID control, comparing the results of ACO design to the results of genetic algorithm design, the final results present that the ACO has the effectiveness and application value of a new simulated evolutionary optimization method[1-3].
Recently, the development of ACO has been researched and paid more attention by researchers in various fields[4-8].There are also many improvements to the ACO algorithm, including: the improvements of algorithm in self-adaptive, the improvements of increasing the diversity of various group, the improvements of enhancing local search, combining with the global optimization algorithm, and combining with deterministic local optimization algorithm, etc. However, with the developments of the theory and technology in multicore computing, how to implement ACO efficiently and paralleled in a multicore computing environment being a new challenge for all researchers. This paper will propose a new ACO algorithm which is oriented the multicore computing based on the multicore computing environment.
Abstract. The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudoparallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.
Metadata is the command center in the process of data warehousing, and which is very helpful to data ETL (Extraction, Transformation and Loading), data storage management, data analysis and data mining. CWM (Common Warehouse Metamodel) is an open industry metadata standard, and which is widely used currently, public meta-models and its rules defined in CWM can properly support data transformation and cleansing. One metadata-driven development approach is proposed to support the design and development of data ETL process. One data transformation module is realized, and which is used to specify how to use metadata in ETL process. Finally, the development process and steps of using CWM to build ETL tool are given.
An improved K-medoids clustering algorithm (IKMC) to resolve the problem of detecting the near-duplicated records is proposed in this paper. It considers every record in database as one separate data object, uses edit-distance method and the weights of attributes to get similarity value among records, then detect duplicated records by clustering these similarity value. This algorithm can automatically adjust the number of clusters through comparing the similarity value with the preset similarity threshold, and avoid a large numbers of I/O operations used by traditional "sort/merge" algorithm for sequencing. Through the experiment, this algorithm is proved to have good detection accuracy and high availability.
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