Name
Title | ||
|---|---|---|
Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach |
Abstract | ||
|---|---|---|
In this paper, we introduce an adaptive evolutionary approach to solve the short-term electrical generation scheduling problem (STEGS). The STEGS is a hard constraint satisfaction optimization problem. The algorithm includes various strategies proposed in the literature to tackle hard problems with constraints such as: the representation used a non-binary coding scheme that drastically reduces the search space compared with the traditional evolutionary approaches. Specialized operators are especially designed for this problem and for this kind of representation, which also includes a local search procedure. Furthermore, the algorithm is guided by an adaptive parameter control strategy. We used some very well known benchmarks for STEGS to evaluate our approach. The results are very encouraging and we have obtained new better values for all the systems tested. Our aim here is to show that evolutionary approaches can be considered as good techniques to be used to solve real-world highly constrained problems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ejor.2005.03.074 | European Journal of Operational Research |
Keywords | Field | DocType |
Evolutionary computations,Short-term electrical generation scheduling,Real-world problems | Constraint satisfaction,Mathematical optimization,Job shop scheduling,Evolutionary algorithm,Adaptive system,Local search (optimization),Adaptive control,Optimization problem,Mathematics,Constrained optimization | Journal |
Volume | Issue | ISSN |
179 | 3 | 0377-2217 |
Citations | PageRank | References |
5 | 0.56 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Maturana | 1 | 5 | 0.56 |
| María Cristina Riff | 2 | 200 | 23.91 |