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The improved variants of Grey wolf optimizer has good exploration capability for global optimum solution.
However, the exploitation competence of the existing variants of grey wolf optimizer is unfortunate. Researchers are
continuously trying to improve the exploitation phase of the existing grey wolf optimizer, but still the improved variants
of grey wolf optimizer are lacking in local search capability. In the proposed research, the exploitation phase of the
existing grey wolf optimizer has been further improved using simulated annealing algorithm and the proposed hybrid
optimizer has been named as hGWO-SA algorithm. The effectiveness of the proposed hybrid variant has been tested for
various benchmark problems including multi-disciplinary optimization and design engineering problems and unit
commitment problem of electric power system and it has been experimentally found that the proposed optimizer performs
much better than existing variants of grey wolf optimizer. The feasibility of hGWO-SA algorithm has been tested for
small & medium scale power systems unit commitment problem. In which, the results for 4 unit, 5 unit, 6 unit, 7 unit, 10
units, 19 unit, 20 unit, 40 unit and 60 units are evaluated. The 10-generating units are evaluated with 5% and 10%
spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared
to other algorithms.
A hybrid neural network and simulated annealing approach to the unit commitment problem
