Splay Tree Hybridized Multicriteria ant Colony and Bregman Divergencive Firefly Optimized Vlsi Floorplanning

Floorplanning is a basic designing step in VLSI circuit to estimate chip area before the optimized placement of digital blocks and their connections. The process of Floorplanning involves identifying the locations, shape, and size of components in a chip. The floorplanning is a hard problem since the consumption of energy and heat generation was high for the placement of modules. In order to improve the optimized floor planning, a novel Splay tree Hybridized Multicriteria Ant Colony and Bregman Divergencive Firefly Optimized Floor Planning (STHMAC-BDFOFP) technique is proposed. Main objective of STHMAC-BDFOFP technique is to efficient floor planning with minimum time. Initially, a number of modules are given with their connections obtained from benchmark dataset. In STHMAC-BDFOFP, a Splay tree-based non-slicing floor planning model constructing trees via modeling geometric relationship among modules. A splay tree is build after performing different operations namely splaying, join, split, insertion, and deletion on modules for floor planning. The constructed floorplan design is optimized by Hybridized Multicriteria Ant Colony and Bregman Divergencive Firefly algorithm. At first, the ant colony optimization is applied for finding the local optimum solution from the population of modules in the Splay tree with Multicriteria functions namely energy consumption, heat generation, space occupied, and wire length. Depends on fitness measure, the local optimum solution is determined. Then the global solution is attained by applying the Bregman Divergencive Firefly ranked algorithm. In this way, optimum modules in the splay tree are identified and obtain efficient floorplanning in VLSI design. Discussed results indicate that STHMAC-BDFOFP technique improves the performance of energy and heat aware floor planning as compared to conventional works.

Enhanced Recommendation System in Community–Question-Answering Websites using Splay-Tree Methodology

In community-driven ranking systems participants with superior scores acquire strong reputation than low scored participants. The community-question-aswering websites, like stackexchange network, participants with unreciprocated or unnoticed questions for a long time get a badge called tumbleweed without taking into account of their earlier period performance. The user-driven question and answering website considers this reward as a consolation prize and discourages them instead of encouraging. Mostly, the users who ask unnoticed questions are either a new or less scored participants. The center of attention of this research work is to propose a recommendation system that prevents unnoticed questions from the participants who are about to receive a tumbleweed badge. A splay-tree is a tree with a self-balancing ability which brings the newly accessed node to the apex of the tree. In this paper, the splay-tree correspond to participants’ ranks and the highlight of the work is to raise average or beneath average scorer to apex without disturbing existing toppers

Comparison of the performance of skip lists and splay trees in classification of internet packets

Due to the increasing number of Internet users and the volume of information exchanged by software applications, Internet packet traffic has increased significantly, which has highlighted the need to accelerate the processing required in network systems. Packet classification is one of the solutions implemented in network systems. The most important issue is to use an approach that can classify packets at the speed of the network and show optimum performance in terms of memory usage. In this study, we evaluated the performance in packet classification of two of the most important data structures used in decision trees, i.e. the skip list and splay tree. Our criteria for performance were the time of packet classification, the number of memory accesses, and memory usage of each event. These criteria were tested by the ACL and IPC rules with different numbers of rules as well as by different packet numbers. The results of the evaluation showed that the performance of skip lists is higher than that of splay trees. By increasing the number of classifying rules, both the difference in the speed of packet classification and the superiority of the performance of the skip list over that of the splay tree become more significant. The skip list also maintains its superiority over the splay tree in lower memory usage. The results of the experiments confirm the scalability of this method in comparison to the splay tree method.

POHON BINER

Salah satu penerapan teori pohon yang paling berguna dan dipakai yaitu konsep binary search tree dimana konsep ini memberikan struktur data yang memudahkan operasi pencarian, penambahan, dan penghapusan terhadap data. Operasi tersebut lebih efisien dan jauh lebih baik pada konsep ini dibanding sequential search pada senarai berkait dalam waktu eksekusi / run-time.Dari konsep binary search tree ini dikembangkan lagi suatu struktur penyimpanan data yang merupakan modifikasi dari binary search tree tersebut yaitu AVL-Tree dan Splay Tree yang masing-masing mempunyai keunggulan pada kasus tertentu yang sekarang ini sering dijumpai.AVL-Tree merupakan modifikasi binary search tree yang tinggi setiap upapohon kiri dan upapohon kanan sama atau setidaknya selisih antara keduanya tidak lebih dari 1. Keunggulan dari AVL-Tree antara lain untuk mengoptimasi pencarian data terutama untuk kasus pohon yang condong ke kiri atau ke kanan sehingga pencarian akan jauh lebih mudah apabila pohon tersebut seimbang. Kasus pohon yang condong ke kiri atau kanan itu mungkin saja terjadi terutama apabila penambahan elemen dan penghapusan elemen dilakukan terus-menerus dan tidak dapat diketahui urutannya.Sedangkan Splay-Tree justru kebalikan dari AVL-Tree yang tidak mempermasalahkan kecondongan upapohonnya namun setiap kali data diakses maka simpul dari data yang diakses tersebut akan dinaikkan keatas mendekati akar pohon. Data yang sering diakses / aktif akan berada dekat pada akar pohon sehingga data tersebut mudah Dengan demikian dapat disimpulkan bahwa penerapan teori pohon sangatlah bermanfaat dalam kajian struktur data.