A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator selection. In the proposed method, the category of each indicator is coded by real numbers, and some illegal chromosomes are repaired by the identification and restoration of empty class. Two mutation operators, namely the discrete random mutation operator and the optimal direction mutation operator, are designed to balance the local convergence speed and the global convergence performance, which are then combined with migration strategy and insertion strategy. For the purpose of verification and illustration, the proposed method is compared with the K-means clustering algorithm and the standard genetic algorithms via a numerical simulation experiment. The experimental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis algorithm. Meanwhile, the proposed clustering analysis algorithm is also applied to select the business cycle indicators to examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly select some leading indicators, coincident indicators, and lagging indicators to reflect the business cycle, which is extremely operational for some macro economy administrative managers and business decision-makers.
When TCP operates in wireless networks, its congestion control algorithms such as fast retransmit/recovery (FRR) and retransmission timeouts (RTO) are often triggered regardless of congestion. Such FRRs/RTOs non-related to congestion incur TCP's misbehavior such as blindly halving the transmission rate, unnecessarily retransmitting the outstanding packets which may be in the bottleneck queue. Although many previous works have been proposed to detect the FRRs/RTOs non-related to congestion, they paid little attention on effectively adjusting the transmission rate according to network conditions. In this paper, we propose a new scheme to dynamically respond to FRRs/RTOs by considering network conditions such as the available bandwidth and the loss rate. Our scheme adjusts the transmission rate in proportion to the available bandwidth in order to quickly and fully utilize the available bandwidth, and then readjusts it in inverse proportion to the loss rate in order to avoid burst losses and long go-back-N retransmissions. Throughout the extensive experiments, we show that our scheme significantly outperforms previous works while it maintains the fair and friendly behavior to other TCP connections.
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