A Comparison Study of Linearly Decreasing Inertia Weight Method and Rational Decrement of Vmax Method for WMNs Using WMN-PSOHC Intelligent System Considering Normal Distribution of Mesh Clients

2021 ◽  
pp. 104-113
Author(s):  
Shinji Sakamoto ◽  
Leonard Barolli ◽  
Shusuke Okamoto
Keyword(s):  
Normal Distribution ◽  
Intelligent System ◽  
Comparison Study ◽  
Inertia Weight ◽  
Weight Method ◽  
Mesh Clients

scholarly journals A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using WMN-PSODGA hybrid intelligent simulation system

2021 ◽  
pp. 1-16
Author(s):  
Admir Barolli ◽  
Kevin Bylykbashi ◽  
Ermioni Qafzezi ◽  
Shinji Sakamoto ◽  
Leonard Barolli ◽  
...  
Keyword(s):  
Load Balancing ◽  
Uniform Distribution ◽  
Simulation System ◽  
Placement Problem ◽  
Chi Square ◽  
Node Placement ◽  
Inertia Weight ◽  
Uniform Distributions ◽  
Weight Method ◽  
Mesh Clients

Wireless Mesh Networks (WMNs) are gaining a lot of attention from researchers due to their advantages such as easy maintenance, low upfront cost and high robustness. Connectivity and stability directly affect the performance of WMNs. However, WMNs have some problems such as node placement problem, hidden terminal problem and so on. In our previous work, we implemented a simulation system to solve the node placement problem in WMNs considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare chi-square and uniform distributions of mesh clients for different router replacement methods. The router replacement methods considered are Constriction Method (CM), Random Inertia Weight Method (RIWM), Linearly Decreasing Inertia Weight Method (LDIWM), Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that for chi-square distribution the mesh routers cover all mesh clients for all router replacement methods. In terms of load balancing, the method that achieves the best performance is RDVM. When using the uniform distribution, the mesh routers do not cover all mesh clients, but this distribution shows good load balancing for four router replacement methods, with RIWM showing the best performance. The only method that shows poor performance for this distribution is LDIWM. However, since not all mesh clients are covered when using uniform distribution, the best scenario is chi-square distribution of mesh clients with RDVM as a router replacement method.

Edge Weight Method for Community Detection on Mixed Scale-Free Networks

2015 ◽  
Vol 24 (02) ◽  
pp. 1540007 ◽  
Author(s):  
Sorn Jarukasemratana ◽  
Tsuyoshi Murata
Keyword(s):  
Normal Distribution ◽  
Community Detection ◽  
Power Law ◽  
The Other ◽  
Detection Methods ◽  
Node Degree ◽  
Edge Weight ◽  
Scale Free ◽  
Scale Free Networks ◽  
Weight Method

In this paper, we proposed an edge weight method for performing a community detection on mixed scale-free networks.We use the phrase “mixed scale-free networks” for networks where some communities have node degree that follows a power law similar to scale-free networks, while some have node degree that follows normal distribution. In this type of network, community detection algorithms that are designed for scale-free networks will have reduced accuracy because some communities do not have scale-free properties. On the other hand, algorithms that are not designed for scale-free networks will also have reduced accuracy because some communities have scale-free properties. To solve this problem, our algorithm consists of two community detection steps; one is aimed at extracting communities whose node degree follows power law distribution (scale-free), while the other one is aimed at extracting communities whose node degree follows normal distribution (non scale-free). To evaluate our method, we use NMI — Normalized Mutual Information — to measure our results on both synthetic and real-world datasets comparing with both scale-free and non scale-free community detection methods. The results show that our method outperforms all other based line methods on mixed scale-free networks.

scholarly journals A PSO algorithm with inertia weight decay by normal distribution

2020 ◽  
Vol 37 (2) ◽  
pp. 208-213
Author(s):  
Haotian XU ◽  
Weidong JI ◽  
Xiaoqing SUN ◽  
Qiang LUO
Keyword(s):  
Normal Distribution ◽  
Pso Algorithm ◽  
Weight Decay ◽  
Inertia Weight
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