Name
Abstract | ||
|---|---|---|
As more and more fossil fuels are burned in order to keep up with the overgrowing demand for energy it is becoming increasingly necessary to look for alternative energy sources. Reducing peak-time energy demand is important to make the best use of renewable energies. In this paper we present an Ant Colony Optimization (ACO) based approach for the problem of scheduling tasks so as to minimize peak-times and cost. This approach is compared with an existing Genetic Algorithm (GA) based approach. ACO managed to obtain very similar results compared with GA, with the cost of the schedules being sometimes slightly better than the Genetic Algorithm approach, especially for shorter execution times. The ACO approach also proved to be more consistent in its results than the GA approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CSE.2015.31 | 2015 IEEE 18th International Conference on Computational Science and Engineering |
Keywords | Field | DocType |
Ant Colony Optimization,Scheduling,Demand-Side Management | Ant colony optimization algorithms,Mathematical optimization,Renewable energy,Computer science,Scheduling (computing),Fossil fuel,Schedule,Alternative energy,Genetic algorithm,Distributed computing,Metaheuristic | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andre Silva | 1 | 0 | 0.34 |
| Joao Marinheiro | 2 | 0 | 0.34 |
| Henrique Lopes Cardoso | 3 | 223 | 34.02 |
| Eugénio Oliveira | 4 | 974 | 111.00 |