scholarly journals A Faster Quick Search Algorithm

Algorithms ◽  
2014 ◽  
Vol 7 (2) ◽  
pp. 253-275 ◽  
Author(s):  
Jie Lin ◽  
Donald Adjeroh ◽  
Yue Jiang
Keyword(s):  
Search Algorithm ◽  
Quick Search

scholarly journals Quick search algorithm of X-ray pulsar period based on unevenly spaced timing data

2013 ◽  
Vol 62 (1) ◽  
pp. 019701
Author(s):  
Zhou Qing-Yong ◽  
Ji Jian-Feng ◽  
Ren Hong-Fei
Keyword(s):  
Search Algorithm ◽  
X Ray ◽  
Pulsar Period ◽  
Timing Data ◽  
Quick Search

Review of Quick Search for Transmission Section

2014 ◽  
Vol 986-987 ◽  
pp. 507-511
Author(s):  
Yan Xie ◽  
Xiao Jun Tang ◽  
Jian Wen Ren ◽  
Chun Zheng Tian ◽  
Lin Lin Yu ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Cascading Failure ◽  
Protection Measures ◽  
Research Status ◽  
Safety Protection ◽  
Definition Of ◽  
Quick Search

This paper introduces the current research status of quick search for transmission section. Firstly, the definition of transmission section is introduced. Secondly, search algorithm for transmission section is presented. Thirdly, the safety protection measures that can prevent cascading failure are introduced. Finally, outlook on quick search for transmission section is proposed.

scholarly journals Parallel Quick Search Algorithm for the Exact String Matching Problem Using OpenMP

2016 ◽  
Vol 04 (13) ◽  
pp. 1-11 ◽  
Author(s):  
Sinan Sameer Mahmood Al-Dabbagh ◽  
Nawaf Hazim Barnouti ◽  
Mustafa Abdul Sahib Naser ◽  
Zaid G. Ali
Keyword(s):  
Search Algorithm ◽  
String Matching ◽  
Matching Problem ◽  
Quick Search

A Fast Search Algorithm for Transmission Section Based on K Shortest Paths

2014 ◽  
Vol 960-961 ◽  
pp. 786-789
Author(s):  
Xian Chao Liu ◽  
Jian Wen Ren ◽  
Wei Dong Qu
Keyword(s):  
Network Topology ◽  
Power Flow ◽  
Search Algorithm ◽  
System Simulation ◽  
Shortest Paths ◽  
Fast Search ◽  
Fast Search Algorithm ◽  
Single Path ◽  
Small Tributary ◽  
Quick Search

A new algorithm based on K shortest paths is presented for quick search of the transmission section, and then the timely electricity network topology is transferred into a directed graph. Using this new algorithm, k shortest paths would be directly searched for; the transmission section influenced greatly by the power flow shift is then obtained quickly. To avoid leaving part of the search because criteria are too small tributary drain election when using a single path to ensure the integrity of the transmission section. Finally CEPRI36 nodes system simulation is analyzed, and the effectiveness of the algorithm is demonstrated.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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