An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem. (English) Zbl 1487.90485
Summary: A new intelligent computing based approach for solving multi-objective unit commitment problem (MOUCP) and its fuzzy model is presented in this paper. The proposed intelligent approach combines binary-real-coded genetic algorithm (BRCGA) and \(K\)-means clustering technique to find the optimal schedule of the generation units in MOUCP. BRCGA is used in order to tackle both the unit scheduling and load dispatch problems. While, \(K\)-means clustering technique is used to divide the population into a specific number of subpopulation with-dynamic-sizes. In this way, different genetic algorithm (GA) operators can apply to each sub-population, instead of using the same GA operators for all population. The proposed intelligent algorithm has been tested on standard systems of MOUCPs. The results showed the efficiency of the proposed approach to solve (MOUCP) and its fuzzy model.
MSC:
| 90C11 | Mixed integer programming |
| 90C59 | Approximation methods and heuristics in mathematical programming |
| 90C70 | Fuzzy and other nonstochastic uncertainty mathematical programming |
| 90C90 | Applications of mathematical programming |
Keywords:
unit commitment problem; intelligent computing; genetic algorithm; dynamic subpopulation structureReferences:
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