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Abstract-The objectives in this paper are twofold: design an approach for the netlist partitioning problem using the cooperative multilevel search paradigm introduced by Toulouse et al. and study the effectiveness of this paradigm for solving combinatorial optimization problems, in particular, those arising in the very large scale integration (VLSI) computer-aided design (CAD) area. The authors present a cooperative multilevel search algorithm CoMHP and describe a parallel implementation on the SGI O2000 system. Experiments on ISPD98 benchmark suite of circuits show, for four-way and eight-way partitioning, a reduction of 3% to 15% in the size of hyperedge cuts compared to those obtained by hMETIS. Bisections of hypergraphs based on the algorithm also outperform hMETIS, although more modestly. The authors present experimental results to demonstrate that the cooperation scheme plays a key role in the performance of CoMHP. In fact, the improvement in the quality of the solutions produced by CoMHP is to a large extent independent of the partitioners used in the implementation of CoMHP. The experimental results also demonstrate the effectiveness of the cooperative multilevel search paradigm for solving the netlist partitioning problem and show that the cooperative multilevel search strategy can be used as a paradigm for designing effective solution techniques for combinatorial optimization problems such as those arising in the VLSI CAD area.

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With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis.

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