Title
A component-based study of energy consumption for sequential and parallel genetic algorithms
Abstract
Recently, energy efficiency has gained attention from researchers interested in optimizing computing resources. Solving real-world problems using optimization techniques (such as metaheuristics) requires a large number of computing resources and time, consuming an enormous amount of energy. However, only a few and limited research efforts in studying the energy consumption of metaheuristics can be found in the existing literature. In particular, genetic algorithms (GAs) are being used so widely to solve a large range of problems in scientific and real-world problems, but hardly found explained in their internal consumption behavior. In the present article, we analyze the energy consumption behavior of such techniques to offer a useful set of findings to researchers in the mentioned domains. We expand our study to include several algorithms and different problems and target the components of the algorithms so that the results are still more appealing for researchers in arbitrary domains of application. Our experiments on the sequential GAs show the controlling role of the fitness operator on energy consumption and also reveal possible energy hot spots in GAs operations, such as mutation operator. Further, our distributed evaluations besides a statistical analysis of the results demonstrate that the communication scheme could highly affect the energy consumption of the parallel evaluations of the GAs.
Year
DOI
Venue
2019
10.1007/s11227-019-02843-4
The Journal of Supercomputing
Keywords
Field
DocType
Energy consumption, Green computing, Genetic algorithms, Sequential, Parallel
Green computing,Computer science,Efficient energy use,Operator (computer programming),Energy consumption,Genetic algorithm,Distributed computing,Metaheuristic,Statistical analysis,Mutation operator
Journal
Volume
Issue
ISSN
75.0
10.0
1573-0484
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Amr Abdelhafez111.36
Alba Enrique21438.74
gabriel luque3727.91