Open AccessGreedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids
Author(s) -
Sami Nasser Lauar,
Mário Mestria
Publication year - 2021
Publication title -
inteligencia artificial
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.149
H-Index - 12
eISSN - 1988-3064
pISSN - 1137-3601
DOI - 10.4114/intartif.vol24iss68pp123-137
Subject(s) - greedy algorithm , news aggregator , genetic algorithm , metaheuristic , mutation , mathematical optimization , computer science , algorithm , population , set (abstract data type) , mathematics , biology , genetics , demography , sociology , gene , programming language , operating system
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%.