GWO-SA: A Novel Hybrid Grey Wolf Optimizer-Simulated Annealing algorithm for Multidisciplinary Design Optimization Problems

Abstract

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 very poor. 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.