A GENETIC ALGORITHM FOR DETECTING COMMUNITIES IN LARGE-SCALE COMPLEX NETWORKS

2010 ◽  
Vol 13 (01) ◽  
pp. 3-17 ◽  
Author(s):  
CHUAN SHI ◽  
ZHENYU YAN ◽  
YI WANG ◽  
YANAN CAI ◽  
BIN WU
Keyword(s):  
Genetic Algorithm ◽  
Complex Networks ◽  
Large Scale ◽  
Fitness Function ◽  
Network Modeling ◽  
Search Space ◽  
Great Flexibility ◽  
Modeling And Analysis ◽  
Research Areas ◽  
Network Modeling And Analysis

Network model recently becomes a popular tool for studying complex systems. Detecting meaningful communities in complex networks, as an important task in network modeling and analysis, has attracted great interests in various research areas. This paper proposes a genetic algorithm with a special encoding schema for community detection in complex networks. The algorithm employs a metric, named modularity Q as the fitness function and applies a special locus-based adjacency encoding schema to represent the community partitions. The encoding schema enables the algorithm to determine the number of communities adaptively and automatically, which provides great flexibility to the detection process. In addition, the schema also significantly reduces the search space. Extensive experiments demonstrate the effectiveness of the proposed algorithm.

scholarly journals Complex Networks in Manufacturing and Logistics: A Retrospect

2021 ◽  
pp. 57-70
Author(s):  
Till Becker ◽  
Darja Wagner-Kampik
Keyword(s):  
Complex Networks ◽  
Stochastic Models ◽  
Network Modeling ◽  
Network Models ◽  
Model Systems ◽  
Modeling And Analysis ◽  
Planning And Control ◽  
Long Time ◽  
And Control ◽  
Network Modeling And Analysis

AbstractThe methodology to model systems as graphs or networks already exists for a long time. The availability of information technology and computational power has led to a renaissance of the network modeling approach. Scientists have collected data and started to create huge models of complex networks from various domains. Manufacturing and logistics benefits from this development, because material flow systems are predetermined to be modeled as networks. This chapter revisits selected advances in network modeling and analysis in manufacturing and logistics that have been achieved in the last decade. It presents the basic modeling concept, the transition from static to dynamic and stochastic models, and a collection of examples how network models can be applied to contribute to solving problems in planning and control of logistic systems.

scholarly journals Innovative Spatial-Temporal Network Modeling and Analysis Method of Air Quality

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 26241-26254 ◽  
Author(s):  
Guyu Zhao ◽  
Guoyan Huang ◽  
Hongdou He ◽  
Qian Wang
Keyword(s):  
Air Quality ◽  
Network Modeling ◽  
Temporal Network ◽  
Analysis Method ◽  
Modeling And Analysis ◽  
Network Modeling And Analysis

scholarly journals A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 758
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Space ◽  
Large Scale Problems ◽  
Processing Power ◽  
Compact Genetic Algorithm ◽  
Estimation Of Distribution ◽  
Feasible Solutions ◽  
Compact Optimization

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and material engineering, networked systems, logistics and scheduling. Recently, Deb and Myburgh proposed an evolutionary algorithm capable of handling a scheduling optimization problem with a staggering number of variables: one billion. However, one important limitation of this algorithm is its memory consumption, which is in the order of 120 GB. Here, we follow up on this research by applying to the same problem a GPU-enabled “compact” Genetic Algorithm, i.e., an Estimation of Distribution Algorithm that instead of using an actual population of candidate solutions only requires and adapts a probabilistic model of their distribution in the search space. We also introduce a smart initialization technique and custom operators to guide the search towards feasible solutions. Leveraging the compact optimization concept, we show how such an algorithm can optimize efficiently very large-scale problems with millions of variables, with limited memory and processing power. To complete our analysis, we report the results of the algorithm on very large-scale instances of the OneMax problem.

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.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

scholarly journals A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach

2017 ◽  
Vol 12 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Majeed Heydari ◽  
Amir Yousefli
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Heuristic Algorithms ◽  
Influential Factors ◽  
Market Basket Analysis ◽  
Binary Programming ◽  
Modeling And Analysis ◽  
Market Basket ◽  
Solution Approach ◽  
Research Areas

Abstract Nowadays market basket analysis is one of the interested research areas of the data mining that has received more attention by researchers. But, most of the related research focused on the traditional and heuristic algorithms with limited factors that are not the only influential factors of the basket market analysis. In this paper to efficient modeling and analysis of the market basket data, the optimization model is proposed with considering allocation parameter as one of the important and effectual factors of the selling rate. The genetic algorithm approach is applied to solve the formulated non-linear binary programming problem and a numerical example is used to illustrate the presented model. The provided results reveal that the obtained solutions seem to be more realistic and applicable.

Sign in / Sign up

Export Citation Format

Share Document