Search

Pigeon-inspired optimization (PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator, and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally, which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity — HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of “exploitation” and “exploration”, so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

In order to alleviate traffic congestion on multilayer networks, designing an efficient routing strategy is one of the most important ways. In this paper, a novel routing strategy is proposed to reduce traffic congestion on two-layer networks. In the proposed strategy, the optimal paths in the physical layer are chosen by comprehensively considering the roles of nodes’ degrees of the two layers. Both numerical and analytical results indicate that our routing strategy can reasonably redistribute the traffic load of the physical layer, and thus the traffic capacity of two-layer complex networks are significantly enhanced compared with the shortest path routing (SPR) and the global awareness routing (GAR) strategies. This study may shed some light on the optimization of networked traffic dynamics.

Some burgeoning web applications, such as web search engines, need to track, store and analyze massive real-time users' access logs with high availability of 24*7. The traditional high availability approaches towards general-purpose transaction applications are usually not efficient or economical enough to store these high-rate insertion-only archived stream data. This paper proposes RAIDB5 (Redundant Array of Independent DataBases level 5) to store and manage these archived streams. We design the read-optimized and write-optimized algorithms which is distinct from the read-optimized ones in RAID5. These algorithms significantly improve the performance of write operations via classifying the data files of databases into live and history files. Based on a Markov model, we compare the performance, availability and cost of RAIDB5 with other traditional database backup modes. The analysis results show that RAIDB5 has better performance at the same cost and has the better performability and lower cost at the same system scale than the classical double replication mode.