جستجوی مقالات مرتبط با کلیدواژه "الگوریتم رقابت استعماری" در نشریات گروه "آب و خاک"

Due to the water situation in the country and on the other hand the increase in water needs, water resources management is important. Since the major share of water consumption is allocated to the agricultural sector, water allocation is one of the issues that should be considered. In the present study, a new and at the same time high-performance method based on the Whale algorithm (WOA), for the optimal allocation of water resources of Sufi-chai irrigation and drainage network located in East Azerbaijan province in the agricultural sector during the statistical year data were used from 2005 to 2013. The whale algorithm is one of the newest meta-heuristic algorithms inspired by the bubble net hunting method performed by humpback whales. The results were compared with the results of the ICA algorithm. The objective function was determined based on each of the products and their performance and the income from each product, and then the maximization of the objective function and the optimal allocation of water resources was performed by WOA and ICA algorithms. The results showed that by increasing the application coefficient to 0.9, the profit from the optimal water allocation is associated with a 30% increase in economic profit. Meanwhile, the WOA algorithm, compared to the ICA algorithm with an allocation of 1720 MCM per year, has 8.90% of the optimal use of water resources in agriculture. In general, the use of intelligent evolutionary methods can be a good solution for optimal water allocation in the agricultural sector.

In this research, the simulation and optimization models are integrated to apply the reservoir hedging policy. Simulation of the studied basin was executed using the WEAP model to operate the Doiraj Dam reservoir located on the Doiraj River. In addition, the multi-objective MOICA model was utilized to optimize the system, in which the first objective function (maximizing the percentage of supplying demands), and the second one (minimizing the violation of allowable capacities of the reservoir during the operation period) were considered. In this regard, the operation modeling from the reservoir was carried out based on the current condition for a 720-month period (October 1960 to September 2019). Finally, by defining the optimization scenario and applying the reservoir hedging policy, the operation optimization of the reservoir was done and the results were compared with the reference scenario results. In this study, by considering 24 decision variables including 12 hedging level variables and 12 hedging coefficient variables, the optimal answers were achieved after 1000 iterations. The results showed that the violation of the allowable capacities did not occurred in any periods of the optimization scenario, while in the reference scenario the reservoir level reached the dead level in sequent months with more water shortage which might lead to the lack of water supply in such months and serious damages to the system. Due to the application of hedging policy in the optimization scenario, the percentage of supplying the demands in the critical months is increased between 20 to 35% as compared to the reference scenario, which indicates a significant reduction in the failure rate in such months.

Regarding climate change, and consequently the decrease in the Gergranod River catchment area, as well as the sharp decrease in the volume of water stored behind the Golestan dam in recent years, a review of the water allocation problem is necessary. Therefore, in this research, the optimal values of water harvesting from the Golestan dam reservoir are calculated using the colonial competition algorithm (ICA), which is one of the interconnected algorithms based on socio-political evolution. In this study, the proper parameters of the colonial competition algorithm were calculated with try and error, and then in five times the implementation of this algorithm in the first run in the 1240 repetition of 2,363.2 million cubic meters for the target function, in the second run in repeat 1256, the value of the target function 4947/2 Million cubic meters, in the third run in Repetition 1271 the value of the objective function was 1,971.1 million cubic meters, the 1300 repetition in the fourth run was 304.1 million cubic meters for the target function, and in the fifth implementation and repeat 1450, the value of the target function was 1,2473.1 million cubic meters Come. The comparison between the values of the objective function indicates that with increasing the number of repetitions, the value of the objective function is reduced. The results indicate that this algorithm is good. The optimal release values obtained from the algorithm have always fluctuated during the statistical period, so that in the first three years of the increasing trend, the fourth and fifth declines, the sixth and seventh increases, the eighth and ninth, and finally increased again in the tenth.

Determining the scouring depth around bridge piers during flood has been considered as one of the main and important parameters in designing the foundations of bridge piers. Thus, it has been the subject of numerous studies to provide precise methods to calculate these elements. Due to the high complexity of this phenomenon and various involved parameters, the utilized empirical relations have not sufficient accuracy and efficiency; accordingly, economic and technical design based on their results is not feasible. In this regard, a new model is provided to estimate the scouring depth around bridge piers using Neuro-Fuzzy Comparative Inference System that optimized by Imperialist Competitive Algorithm. Comparing the results of proposed model with the results of base models showed that the accuracy and efficiency of the base models increased significantly with these optimizations. In addition, comparing the results of proposed models with the results of empirical relations from Mississippi, HEC-18, Laursen & Toch, and Froehlich showed that proposed model has significantly better accuracy in comparison with mentioned experimental relations.

Numerous studies in optimization of water supply networks have been allocated to cost reduction of these infrastructures. In general optimization methods, minimizing the costs is the unique purpose, but applying this view point for the case of water supply systems may lead to reduction of the utility and efficiency of the system. Therefore, some multi-objective optimization models have been used to decrease the large amount of costs in these systems and to increase their operation efficiency. The present study has attempted to illustrate a case study for a town of Kerman in order to decrease the costs of rebuilding pipe network using the Imperialistic Competition Optimization Algorithm. So, the network simulation was performed with Water GEMS software. Firstly, the water supply network of the study area was simulated in the Water GEMS model. Next, the properties of water supply network were inserted. Then, using the standard pressure and speed constraints, the optimal options of the algorithm were obtained. By re-running the results of this optimized model in the Water GEMS model and the limits’ checkup, the total costs were estimated and compared. Analysis of the optimal model results showed that in comparison with the state prior to the network optimization, the Imperialistic Competition Algorithm has significantly reduced the cost function of the network.

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.

Non-linear Muskingum model is an efficient method for flood routing. However, the efficiency of this method is influenced by three applied parameters. Therefore, efficiency assessment of Imperialist Competition Algorithm (ICA) to evaluate optimum parameters of non-linear Muskingum model was addressed in this study. In addition to ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were also used to find an available criterion to verify ICA. In this regard, ICA was applied for Wilson flood routing; then, routing of two flood events of DoAab Samsami River was investigated. In case of Wilson flood, the target function was considered as the sum of squared deviation (SSQ) of observed and calculated dischargem. Routing two other floods, in addition to SSQ, another target function was also considered as the sum of absolute deviations of observed and calculated discharge. For the first floodwater based on SSQ, GA indicated the best performance; however, ICA was in the first place, based on SAD. For the second floodwater, based on both target functions, ICA indicated a better operation. According to the obtained results, it can be said that ICA could be recommended as an appropriate method to evaluate the parameters of Muskingum non-linear model.