Ant colony optimization for community detection in large-scale complex networks

2011 ◽  
Author(s):  
Dongxiao He ◽  
Jie Liu ◽  
Dayou Liu ◽  
Di Jin ◽  
Zhengxue Jia
Keyword(s):  
Complex Networks ◽  
Ant Colony Optimization ◽  
Community Detection ◽  
Large Scale ◽  
Ant Colony

A multi-objective ant colony optimization with decomposition for community detection in complex networks

2019 ◽  
Vol 41 (9) ◽  
pp. 2521-2534 ◽  
Author(s):  
Ruochen Liu ◽  
Jiangdi Liu ◽  
Manman He
Keyword(s):  
Complex Networks ◽  
Ant Colony Optimization ◽  
Community Detection ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Network Clustering ◽  
Multi Objective ◽  
Search Mechanism ◽  
Large Scale Networks

Community detection in complex networks plays an important role in mining and analyzing the structure and function of networks. However, traditional algorithms for community detection-based graph partition and hierarchical clustering usually have to face expensive computational costs or require some specific conditions when dealing with complex networks. Recently, community detection based on intelligent optimization attracts more and more attention because of its good effectiveness. In this paper, a new multi-objective ant colony optimization with decomposition (MACOD) for community detection in complex networks is proposed. Firstly, a new framework of multi-objective ant colony algorithm specialized initially for the complex network clustering is developed, in which two-objective optimization problem can be decomposed into a series of subproblems and each ant is responsible for one single objective subproblem and it targets a particular point in the Pareto front. Secondly, a problem-specific individual encoding strategy based on graph is proposed. Moreover, a new efficient local search mechanism is designed in order to improve the stability of the algorithm. The proposed MACOD has been compared with four other state of the art algorithms on two benchmark networks and seven real-world networks including three large-scale networks. Experimental results show that MACOD performs competitively for the community detection problems.

scholarly journals Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks

2012 ◽  
Vol 23 (3) ◽  
pp. 451-464 ◽  
Author(s):  
Di JIN ◽  
Bo YANG ◽  
Jie LIU ◽  
Da-You LIU ◽  
Dong-Xiao HE
Keyword(s):  
Random Walk ◽  
Complex Networks ◽  
Ant Colony Optimization ◽  
Community Detection ◽  
Ant Colony

scholarly journals QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

2021 ◽  
Vol 14 (1) ◽  
pp. 270-280
Author(s):  
Abhijit Halkai ◽  
◽  
Sujatha Terdal ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Large Scale ◽  
Cluster Head ◽  
Particle Swarm ◽  
Base Station ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

scholarly journals Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 3126
Author(s):  
Desheng Lyu ◽  
Bei Wang ◽  
Weizhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Local Search ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

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