scholarly journals Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

2018 ◽  
Vol 9 (3S) ◽  
pp. 730 ◽  
Author(s):  
A. Zabidi ◽  
I.M. Yassin ◽  
N.M. Tahir ◽  
Z.I. Rizman ◽  
M. Karbasi
Keyword(s):  
Autoregressive Model ◽  
Artificial Bee Colony ◽  
Model Structure ◽  
Swarm Optimization ◽  
Structure Selection ◽  
Bee Colony ◽  
Nonlinear Autoregressive Model ◽  
Model Structure Selection ◽  
Nonlinear Autoregressive ◽  
Selection Of

Research on intelligent clustering of male upper body

2021 ◽  
pp. 004051752110001
Author(s):  
Pengpeng Cheng ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Jianping Wang ◽  
Daoling Chen
Keyword(s):  
Artificial Bee Colony ◽  
Young Men ◽  
Cuckoo Search ◽  
The Body ◽  
Upper Body ◽  
Intelligent Algorithm ◽  
Density Peak ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Length Width

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya
Keyword(s):  
Artificial Bee Colony ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Swarm Optimization ◽  
Content Type ◽  
Bee Colony ◽  
Glowworm Swarm Optimization ◽  
Optimal Cluster

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

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