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

In this study, the performance of the algorithms of Whale (WOA), Differential Evolution (DE), Crow Search (CSA), and Gray Wolf optimization (GWO) were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water demands. After defining the objective function and its constraints, the performance of the algorithms was compared with each other and with the absolute optimal values obtained by 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, VIKOR, LINMAP, CODAS, ELECTRE I and Simple Additive Weighting after obtaining the performance evaluation indices of each algorithm (Reliability, reversibility, and vulnerability). Finally, integration methods (Mean, Borda, and Copeland techniques) were used to evaluate the performance of models. The results showed that the average responses of the GWO, WOA, DE, and CSA were 1.08, 1.49, 1.29 and 1.19 times the absolute optimal response and the answers’ coefficient of variation obtained by GWO was 2,113 and 1.43 times smaller than the WOA, DE, and CSA, respectively. Moreover, all integration techniques indicated the superiority of the GWO. Then, the CSA, DE, and WOA 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, VIKOR and LINMAP methods.