Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm-Based Cluster Head Selection Scheme for WSNs

2021 ◽  
Author(s):  
Rambabu Bandi ◽  
Venugopal Reddy Ananthula ◽  
Sengathir Janakiraman
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Cluster Head ◽  
Search Strategies ◽  
Cluster Head Selection ◽  
Selection Scheme ◽  
Bee Colony

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.

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

2017 ◽  
Vol 9 (4) ◽  
pp. 71-91 ◽  
Author(s):  
Praveen Kumar Reddy Maddikunta ◽  
Rajasekhara Babu Madda
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Cluster Head ◽  
High Energy ◽  
Superior Performance ◽  
Cluster Head Selection ◽  
Bee Colony ◽  
Iot Devices

Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

Artificial bee colony algorithm with multiple search strategies

2015 ◽  
Vol 271 ◽  
pp. 269-287 ◽  
Author(s):  
Wei-feng Gao ◽  
Ling-ling Huang ◽  
San-yang Liu ◽  
Felix T.S. Chan ◽  
Cai Dai ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategies ◽  
Bee Colony

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

2020 ◽  
pp. 1347-1367
Author(s):  
Praveen Kumar Reddy Maddikunta ◽  
Rajasekhara Babu Madda
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Gravitational Search Algorithm ◽  
Cluster Head ◽  
Cluster Head Selection ◽  
Energy Aware ◽  
Bee Colony ◽  
Iot Devices

Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

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