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

Abstract

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%.

Indoor Localization Algorithm Based on Artificial Neural Network

“…The center vector of the Wifi fingerprint dataset is taken as the Wifi fingerprint without saving the whole dataset, and it is experimentally proven that this method can achieve a correct rate of more than 90% when the acquisition points are separated by a distance of 2 meters [13]. The advantage of using this method is that a large number of Wifi fingerprint datasets can be reduced and the datasets can be saved to mobile terminals, thus enabling offline localization; after the user Wifi list data is sent to the server, the server makes the Wifi fingerprints of the user's location and its vicinity into the client Wifi fingerprint dataset, and the ware requests the fingerprints near the next location to continue offline localization, and Wifi fingerprint localization can be applied to a large number of users positioning methods [14][15].…”