Using composite ranking to select the most appropriate Multi-Criteria Decision Making (MCDM) method in the optimal operation of the Dam reservoir

In this study, the performance of the algorithms of whale, Differential evolutionary, crow search, and Gray Wolf optimization were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water needs. Also, after defining the objective function and its constraints, the convergence degree of the algorithms was compared with each other and with the absolute optimal values obtained from GAMS nonlinear programming method (19.41). These values together with each algorithm optimization results were ranked using six multi-criteria decision-making methods of TOPSIS, VICOR, Linmap, Codas, ELECTRE and Simple Additive Weighting after obtaining the performance evaluation criteria of each algorithm (Reliability, reversibility, and vulnerability). Finally, integration methods (Mean, Borda, and Copland techniques) were used to evaluate the performance of decision models. The results showed that the mean responses of the gray wolf, the whale, differential evolutionary, and crow search algorithms were 1.08, 1.49, 1.29 and 1.19 times the absolute optimal response and the answers’ coefficient of variation obtained by Gray Wolf algorithm was 113.2, and 1.43 times smaller than the whale, differential evolutionary, and crow search algorithms, respectively. Moreover, all integration techniques indicated the superiority of the gray wolf algorithm. Then, the Crow search, Differential evolutionary, and whale algorithms were ranked second to fourth, respectively. On the other hand, the use of these methods in solving the problem of Golestan Dam reservoir optimization was considered appropriate due to the similarity of the results obtained from the integration techniques with the results of TOPSIS, VICOR and Linmap methods.