meta-heuristics algorithm

In this paper, the artificial gorilla troops (GTO) optimizer algorithm with the gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms was compared for optimal operation of dam reservoir. The results of the GTO were evaluated with GWO and PSO results which are successful in complex engineering issues and reservoir operation. The objective function of minimizing the total squares of the downstream demand deficit was defined during the operation period. The constraints of the operating equation include the reservoir continuity equation, the reservoir volume, and the release volume. A case study of Jamishan reservoir dam located in Kermanshah province was considered. In this regard, runoff values related to the statistical years 1991-2011 were introduced as input to the reservoir for optimal operation management. The obtained results from optimization algorithms using error estimation indices including mean square root of error (RMSE), mean absolute value of error (MAE), Nash-Sutcliffe criterion (NSE), ratio of root mean square error to standard deviation of observational data (RSR), Reliability, Resiliency, Vulnerability And the minimization values of the objective function were evaluated. The values of these indicators for GTO were 2.86, 1.85, 0.73, 0.52, 69%, 36%, 23% and 4.7, respectively. The results showed that the GTO algorithm had very good accuracy in minimizing the objective function and based on the values of proposed indicators performed better than the GWO and PSO algorithms. Based on this algorithm, the amount of water release volume was brought to the reservoir as a function of the reservoir volume and the amount of water demand volume for the months of the year was determined.

Hydropower energy is the third largest source of electricity generation and also the most important renewable energy producer in the world. For this reason, the optimal use of this huge and expensive source is essential. In this study, Moth Swarm Metaheuristic Algorithm (MSA) was used for optimization of the hydropower operation of Karun-4 reservoir located in Karun basin for a period of 106 months (from October 2010 to July 2019). After verifying the reliability of the MSA algorithm using several standard benchmark functions, a model was developed for optimal hydropower operation of the Karun-4 reservoir. Also, the results of the algorithm were compared with the results of known metaheuristic algorithms of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The MSA, GA and PSO algorithms were capable to generate energy 105853, 105697.4, and 105841.6 MW, respectively, compared with the actual value 64987.54 MW, during the statistical period. Also, the objective function value for MSA, GA and PSO algorithms, was obtained 0.147, 0.3026, and 0.1584, respectively. The results revealed that the MSA algorithm was the superior algorithm in optimal hydropower operation.