Improved Search Algorithm Based on Probability of Node's Degree on Complex Networks

2012 ◽  
Author(s):  
Yiran Gu ◽  
Wenwen Zhao
Keyword(s):  
Complex Networks ◽  
Search Algorithm

Practical k-agents search algorithm towards information retrieval in complex networks

2018 ◽  
Vol 22 (2) ◽  
pp. 885-905
Author(s):  
Pengfei Liu ◽  
Minyu Feng ◽  
Ming Liu
Keyword(s):  
Information Retrieval ◽  
Complex Networks ◽  
Search Algorithm

scholarly journals Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 15-32
Author(s):  
Ehsan Ardjmand ◽  
William A. Young II ◽  
Najat E. Almasarwah
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Academic Community ◽  
Detection Methods ◽  
The Unconscious ◽  
Comprehensive Review ◽  
Wide Range

Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

scholarly journals A Degree-first Greedy Search Algorithm for the Evaluation of Structural Controllability of Real World Directed Complex Networks

2014 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Debayan Das ◽  
Ayan Chatterjee ◽  
Nabamita Pal ◽  
Amitava Mukherjee ◽  
Mrinal Kanti Naskar
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Search Algorithm ◽  
Greedy Search ◽  
Structural Controllability

Complex Networks

2009 ◽  
Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  
Complex Networks

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.

Sign in / Sign up

Export Citation Format

Share Document