Presenting a new hybrid evolutionary algorithm in optimizing reservoir operation based on new combinative distance-based assessment (CODAS)

Background and objectives: Changes in meteorological and hydrological patterns have led to the use of water resources management tools to find a suitable solution for optimal reservoir operation. Regarding to the inability of conventional optimization methods in solving the complex optimization problems, the use of meta-heuristic algorithms has been considered more than before.
Materials and Methods: In the present study, a combined model of Crow Search (CSA) and Gray Wolf (GWO) Optimization algorithms called Gray Wolf - Crow Search Hybrid algorithm was introduced for the first time in the field of reservoir operation optimization. And its performance was evaluated in comparison with its constituent elements as a powerful tool for optimizing the operation of the single reservoir system of Golestan Dam, considering the objective function (providing downstream water demand). To compare the convergence and performance of these algorithms, the statistical parameters of each algorithm were calculated and compared with each other, as well as with the solution of
non-linear problem solving model (i.e., GAMS Software). Then, in order to analyze the performance of the algorithms, the Combinative Distance-based Assessment (CODAS) Multi-Criteria Decision Making Model was used to rank the decision alternatives (e.g., optimization algorithms) based on volumetric and time based reliability, reversibility, vulnerability criteria and the optimized objective function.
Results: The results suggest that the GWOCSA hybrid approach has a response closer to the absolute optimal value, with an average response rate of 93% of the absolute optimal response and an average of 92% and 83% of the GWO and CSA ones. In addition, the correlation coefficient in the hybrid algorithm is 23 and 1.67 times lower than that of the gray wolf and the crow search algorithm, respectively. On the other hand, the GWOCSA hybrid algorithm performs the best, except in terms of reversibility index in other indicators.
The CODAS Multi-Criteria Decision Making Model also identified the GWOCSA algorithm as the first to solve the problem of the reservoir operation compared to the other two passive algorithms. The gray wolf and the crow search algorithm then rank second and third, respectively.
Conclusion: The CODAS Multi-Criteria Decision Making Model identifies the GWOCSA algorithm in optimizing the objective function better than its constituent algorithms, namely CSA Optimization and GWO algorithm. And the gray wolf and crow search algorithm are then ranked second and third, respectively, so that not only is GWOCSA better at finding the optimal answer, but it also improves performance and increases the efficiency of the hybrid algorithm according to the model performance evaluation indicators.