Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids




Optimization, Metaheuristics, Smart Grids, Set Covering Problem, Genetic Algorithms, Greedy Algorithms


In this work, we present a metaheuristic based on the genetic and greedy algorithms to solve an application of the set covering problem (SCP), the data aggregator positioning in smart grids. The GGH (Greedy Genetic Hybrid) is structured as a genetic algorithm, but it has many modifications compared to the classic version. At the mutation step, only columns included in the solution can suffer mutation and be removed. At the recombination step, only columns from the parent’s solutions are available to generate the offspring. Moreover, the greedy algorithm generates the initial population, reconstructs solutions after mutation, and generates new solutions from the recombination step. Computational results using OR-Library problems showed that the GGH reached optimal solutions for 40 instances in a total of 75 and, in the other instances, obtained good and promising values, presenting a medium gap of 1,761%.


Download data is not yet available.


Metrics Loading ...

Author Biographies

Sami Nasser Lauar, Instituto Federal Do Espírito Santo, Brasil

Electrical Engineering student at Ifes (Federal Institute of Espirito Santo), Brazil

Mario Mestria, Instituto Federal Do Espírito Santo, Brasil

Master of Electrical Engineering from the Federal University of Espírito Santo (1995). Doctor (2011) in Computer Science from the Fluminense Federal University.
Have experience in the areas of Computer Science and Electrical Engineering, with emphasis on
Combinatorial Optimization, Computational Intelligence, and Operational Research.
Working with topics as follows: metaheuristics, hybrid methods, location problems,
clustered traveling salesman problem, layout of manufacturing, and set covering problem.




How to Cite

Nasser Lauar, S., & Mestria, M. (2021). Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids. Inteligencia Artificial, 24(68), 123–137.