Immune system memetic algorithm for power distribution network design with load evolution uncertainty
| Autor: | Bruno Brito Pereira de Souza, Oriane M. Neto, Eduardo G. Carrano, Ricardo H. C. Takahashi |
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| Rok vydání: | 2011 |
| Předmět: |
Mathematical optimization
Branch and bound Population-based incremental learning Evolutionary algorithm Energy Engineering and Power Technology Memetic algorithm Out-of-kilter algorithm Suurballe's algorithm Electrical and Electronic Engineering Difference-map algorithm Algorithm Mathematics FSA-Red Algorithm |
| Zdroj: | Electric Power Systems Research. 81:527-537 |
| ISSN: | 0378-7796 |
| DOI: | 10.1016/j.epsr.2010.10.011 |
| Popis: | A recent paper, [1] , has proposed a methodology for taking into account uncertainties in the load evolution within the design of electric distribution networks. That paper has presented an immunological algorithm that is used for finding a set of solutions which are sub-optimal under the viewpoint of the “mean scenario” load conditions, and which are submitted to a sensitivity analysis for the load uncertainty. This paper presents a further development of the algorithm presented in [1] , employing now a memetic algorithm (an algorithm endowed with local search operators) instead of the original immunological algorithm. The new algorithm is shown to present a better behavior, achieving a better set of candidate solutions, which dominate the solution set of the former algorithm. The solution set of the proposed algorithm is also stable, in the senses that: (i) the same set of solutions is found systematically; and (ii) the merit function values associated to those solutions vary smoothly from one solution to another one. It can be concluded that the design procedure proposed in [1] should be performed preferentially with the algorithm proposed here. |
| Databáze: | OpenAIRE |
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