ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

2021 ◽  
Author(s):  
B. Hariharan ◽  
R. Siva ◽  
S. Kaliraj ◽  
P. N. Senthil Prakash
Keyword(s):  
Energy Efficient ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Vm Consolidation

Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

2012 ◽  
pp. 291-304
Author(s):  
Hamid Ali ◽  
Waseem Shahzad ◽  
Farrukh Aslam Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Efficient ◽  
Efficient Solution ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Battery Power ◽  
Energy Efficient Clustering

In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.

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