الگوریتم های فرا ابتکاری

Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

The cost of dam construction is very high. A lot of dams are broken due to the water passing through dam crest. The most important factor which causes this is, insufficient spillway capacity. The aim of present study was investigating optimal height and length for Ogee-Crested spillway because such height and length minimizes the cost of spillway construction. In order to determine objective function, the spillway length variations were considered in the range of 10 to 30 m and the flood return period was estimated in the range of 1000 to 10,000 years. Optimization was done with using genetic algorithm in MATLAB software environment. In fact, the aim of this study was investigating the effect of height and length of spillway on the cost of spillway construction, therefore, Analytical relationships presented with using regression model. The main function of Ogee-Crested spillway is providing ideal conditions for passing designed flood from upstream to downstream. Bagatur & Onen in 2016, used gene expression planning (GEP) models as an alternative approach to predicting appearance features and spillway design coefficients and therefore, proposed new relationship, for Ogee-Crested spillway. It was found that GEP is much better than regression model for predicting Ogee-Crested spillway characteristics. Haghbin in 2015 proposed that using multi-objective genetic algorithm can optimize spillway geometrical dimensions. The results of this study provide the appropriate height and length for the spillway.

The question of this research is, what is the appropriate height and length for the Ogee-Crested spillway of Ballarood Dam? In this study, the Ballarood Reservoir Dam was selected as a case study and genetic algorithm process was used for optimizing this issue. First, objective function was coded and then, this function was added to the MATLAB software. In order to provide Spillway cost function, spillway length variations were considered within the range of 10 to 30 m. In modeling with using genetic algorithm method, first, the length component was divided into discrete data which included integer numbers from 10 to 30.Keeping in view each optimization problem has its decision variables, constraints, and objective function. The decision variables of this present study, namely the Ballarood Dam spillway decision variables, included height (P), length (L), and flood return period (T). The models derived from algorithm modeled with using regression, and their meaningful level was obtained.

In this study, after determining objective function and choosing suitable operators, optimization was done with using genetic algorithm; the most appropriate response was reported as problem answer. Considering the maximum design load (Hd = 50.50 m) and (T = 10000 years), the optimum dimensions and costs were obtained by function: The optimal spillway length and height was was found to be 14.4 m and 47.31 m respectively.It needs to be mentioned that the designed Ballarood Dam spillway is 20 meters in length. With optimization by genetic algorithm, this length was reduced to 14.4 meters. Comparison of the results showed that if this amount of spillway had been applied during construction of the dam, it could have saved up to 28% on project implementation economically. The output value for the spillway height in this study was 31.47 m and the operational value for the height was 47.7 m. Comparison of these two data indicates 34% higher altitude allocation for the margin of confidence.

One of the goals of this study was reducing costs.  Keeping this in mind, the operational cost for the spillway length and height respectively were 31% and 28% more than designed values, with reduction of spillway length and height, operational costs was greatly reduced. The other goal of this study was   optimizing dimensions of spillway but according to the principles of genetic algorithm, we could not achieve to 100% of this goal. In the other words, the genetic algorithm determines the optimal dimensions but it does not determine the best answer. According to this fact, we can find other optimal dimensions for this problem, which is possible by adding more constraints to the problem. The genetic algorithm has some weaknesses, which include: Extremely high sensitivity to the population size and mutation operators which imposes high costs on the whole project in high reliability dam construction projects. Therefore, we recommend in future dam construction studies the scholars take into consideration the issue of benefits and costs future projects along with consideration of and also the high factor of safety in dam construction projects.

Background and Optimal utilization of water resources systems and the formulation of appropriate rules and policies for the exploitation of reservoirs have been considered by water resource experts in recent years and extensive research has been carried out on them. Although much progress has been made in terms of problem-solving strategies and computational tools, the problem of optimizing the operation of a multi-reservoir systems due to the effect of upstream storage capacities on low-drain inputs is so complicated. Routine optimization methods due to high constraints, discontinuous space and non-linear nature of water resource management issues are not a good tool for solving such problems. For this reason, the metaheuristic optimization algorithms have been considered by researchers. The purpose of this study was to examine and compare the results of applying GA and PSO methods in optimum utilization of Golestan and Bustan multi-grout systems in Gorgan Rood watershed using the reliability index in climate change conditions. In this research, the performance of the GA and PSO in solving the problem of optimizing the operation of a multi-reservoir system including Bostan and Golestan dams located in Gorgan-Rood watershed has been studied and compared. The survey of the entrance to the two dam reservoirs in the year 2014-2015 shows that due to the climate change, the annual input to the Bostan and Golestan dams has decreased by 17% and 60%, respectively. Genetic algorithm is a parallel and guided search based on the theory of evolution. The operators of the GA algorithm include selection, crossover, and mutations that are used up to the next generation, respectively. In PSO optimization algorithm, based on the birds and fishes movements, a number of particles are propagated in the search space and the value of the objective function is calculated in proportion to the position of each particle. Then the new particle position is calculated using the combination of current particle locations and the best place previously used. Achievements: The best answer of the PSO algorithm during the 10 runs is 909.95 and the worst is the equal to 930.53, while the best answer of the GA algorithm during the 10 run is 931.17 and the worst It was 957.32. The comparison of the mean of the answers also show that the PSO algorithm has a 3% advantage over GA. The PSO algorithm has a better performance than GA, so that the PSO algorithm with a reliability of 49.38% has a better performance than the GA algorithm with a reliability of 48.44%.

One of the main challenges hindering optimal water resources management is the problem of optimal exploitation of dam reservoirs. Different methods have been used for optimization problems, but since conventional optimization methods are unable to solve complicated optimization problems, the metaheuristic algorithms are increasingly focused on. As such, the present study uses a genetic metaheuristic algorithm for optimizing Chah-Nimeh water wells of the Sistan region. The main objective is to optimally allocate the water of Chah-Nimeh reserves to users in the Sistan region using a genetic algorithm in the context of managerial scenarios with an emphasis on the scenario of dust stabilization. The results of the genetic algorithm show that in the first year, the optimal discharge rate is 20.3531 million m3 and the demand is 98.3121 million m3, so 77.959 million m3 of the demand is unsatisfied. The results reveal that the genetic algorithm could give a proper optimal answer. Furthermore, it is found that in dust stabilization scenario, shortly after the implementation of the scenario, the discharge will not be possible for the agricultural sector anymore (by 2010).

Optimization of the exploitation of dam reservoir is considered as one of the most vital problems in field of water science engineering. This problem has been solved through common optimization methods at present time. One of the most important approaches of optimization is the utilization of meta-heuristic algorithms. In this paper, the imperialist competitive algorithm has been used to solve optimization of exploitation from dam reservoir. The approach has been taken in this study is the application of continuity equation in order to determine the initial position of each country that is named as chain constraints. The results of applying the chain constraints and lack of application of the chain constraints have been compared and consequently these results have been compared with one of well -known Algorithm named as Ant Colony Algorithm. The results indicated that imperialist competitive algorithm without considering the continuity equation , rarelyable to find possible answer and applying the chain constraints to determine the initial position of countries, enhanced the performance of algorithm more efficiently and it leads to even better performance compared to ant colony algorithm, and find an appropriate value for the objective function, so that after running ten times, the mean for objective function for imperialist competitive algorithm was 15.822 and for ant colony algorithm was 48.008.

One of the most important problems in water resources management is the optimal operation of dam reservoirs. Various methods have been applied to deal with optimization problems. Noting the inability of the conventional optimization methods to solve complex optimization problems, the Meta-Heuristic algorithms have been noticed more than ever. In this study, particle swarm, genetic and continuous ant colony system algorithms were used to optimize the operation of the Doroodzan reservoir. Furthermore, the effect of chain constraints inclusion on the framework of these algorithms were analyzed. The results indicated that regarding the chain constraints, all programs resulted in feasible solutions, where without chain constraints, in some cases the algorithm was unable to find a feasible solution. Therefore, applying these constraints in the framework of the algorithms improved their performances further. In finding the optimized solution, the Genetic algorithm had a better performance than the two other algorithms. Reliability criterion was used to evaluate the algorithms performances. Based on this criterion, as one of the most important criteria in determining system performance, the Genetic algorithm with the Reliability value of 0.964 had the most suitable performance.