particle swarm optimization algorithm

Optimum operation of dam reservoirs is one of the most significant management factors in developing the annual resource and consumption plan of dam reservoirs during operation. The decisions regarding amount of water release are made by having the volume of  the reservoir, amount of demand, and the prediction of reservoir inflow in the actual operation of dam reservoirs. Since the volume of release is related to the storage volume of the reservoirs of the dams and should be optimized simultaneously, after introducing the genetic algorithm and the particle swarm algorithm, the performance of these algorithms alone and in combination with each other in the optimal operation of the Alavian dam reservoir are compared with the modeling results in the nonlinear programming and the rule curves of the operation are developed in this study. The performance indicators of the reservoir were been used including reliability, vulnerability and stability  to evaluate the performance of the examined algorithms in the optimal operation of the reservoir.

In this study, after introducing the genetic algorithm and the particle swarm algorithm, innovatively examines the accuracy and effectiveness of modeling by comparing the performance of these algorithms both individually and in combination. This comparison focuses on optimizing the operation of the Alavian dam reservoir over multi-step ahead, using modeling results from the software Lingo. To enhance decision-making for improved management of the Alavian dam reservoir, operation rule curves have been developed. The model utilizes a series of 25 years of data from the Alavian dam, which includes the volume of inflow, the volume of release from the reservoir, storage volume, and usage data encompassing drinking, agriculture, industry, and environmental needs. Additionally, information such as the volume of overflow from the dam reservoir and the volume of evaporation from the surface of the Alaviyan Dam reservoir has been collected on a monthly basis.

The results from these optimal solutions indicate that the combined algorithm outperforms other methods, demonstrating a better correlation with the reservoir management policy. Over the last 25 years, the combined algorithm met 85% of the water requirements for agriculture downstream of Alavian dam, compared to 82% for the Particle swarm optimization(PSO)  algorithm and 78% for the genetic algorithm (GA). In contrast, the nonlinear programming (NLP) method met 80%. The total shortages over the entire 25-year operational period for the GA, PSO, GA-PSO, and NLP algorithms were 38, 33.7, 27.1, and 35.2 million cubic meters, respectively. The GA-PSO algorithm has successfully addressed 10.87 million cubic meters more than the GA algorithm and 6.57 million cubic meters more than the PSO algorithm.

Investigating the results obtained from the optimal solutions revealed that the hybrid algorithm model provides a more favorable result and shows a better correlation regarding the reservoir operation policy. The results indicate the high performance of the hybrid algorithm compared to other studied methods in the optimal operation of the single reservoir system of Alavian dam. Accordingly, the optimal parameters of the Alavian dam reservoir were obtained using a hybrid algorithm. It was proposed to  release volume rule curves and reservoir volume for the  multi-step ahead.

Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the necessary policies. This study aims to propose a hybrid method involving logistic regression with particle swarm optimization algorithm with real value to predict the behavior of dam equipment.

In this study, from 365 days data, from 04/20/2018 to 04/20/2019, of which 600 sets of dam equipment data including parameters of water temperature, water level, valve pressure, sedimentation rate, pore pressure, air temperature, inlet water volume, specific dam characteristics, concrete conditions, reservoir water level, horizontal and vertical displacement, transmission connection components and ground acceleration, strength, pressure, tensile and high stress were used for modeling. Real value-logistic regression and 120 datasets were used for modeling the should be added of particle group optimization algorithm. To evaluate the performance of the proposed method, four statistics including coefficient of determination (R2), root mean square error (RMSE), scattering coefficient (SI), and means bias error (MBE) were used.

The results showed that the model has an acceptable performance in predicting piezometric pressure in the dam body. Also, the results of the artificial neural network model show acceptable convergence with R2 = 0.930 and SSI = 8.587. The results related to the training data of the model also indicate that the mean (µ) and standard deviation (σ) of the proposed model are equal to 1.341 and 1.526 for the training data and these values for the validation data are equal to 1.576 and 2.247, respectively indicating the good performance of the proposed model. In the cumulative probability criterion, the proposed model with P50 = 0.940 and P90 = 1.742 indicates that the results are acceptable.

The results indicate that the real value-logistic regression particle swarm optimization implements the principle of structural risk reduction instead of minimizing the experimental risk that provides excellent generalization for small sample sizes. The ratio of predicted piezometric values ​​to read values ​​for about 72% of the data in this model is about one, indicating the appropriate training and predictive power of this model. Finally, according to the evaluation criteria, the hybrid model performs better than the presented methods.

Groundwater level prediction is an essential priority for planning and managing groundwater resources. This study aimed to compare the accuracy of the Neuro-Fuzzy Adaptive Inference System (ANFIS) model with the ANFIS model combined with particle swarm optimization algorithm (ANFIS+PSO) in predicting the monthly groundwater level of Golpayegan aquifer during 2002-2019. For this purpose, monthly data on rainfall, temperature, pan evaporation in the selected meteorological stations, discharge volume of exploitation wells and groundwater level of observation wells have been used. After spatial and temporal analysis, four observation wells with two input data structures (S1 and S2) were selected to predict the groundwater level. The results of trend and homogeneity tests show a 99% significance of groundwater level changes in the selected observation wells 4, 8, 19 and 20 with a sudden drop of 22, 17, 27 and 2 meters before and after June, September, July and August 2010, respectively. The highest and the lowest accuracy of groundwater level prediction is related to observation wells 20 and 4 with root mean square error values (RMSE) of 2.37 and 0.21 m, respectively, related to ANFIS_S1 and ANFIS + PSO_S2 models. Generally, the results of this study indicate that the selection of appropriate structure of input data is more effective than the combination of two models (ANFIS and PSO) in increasing the accuracy of groundwater level prediction. So, that the optimal structure of input data and the combination of optimized algorithm model have increased the accuracy of groundwater level prediction, 44% and 25%,  respectively.

Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous L-moment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchments' physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesn't necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area.

The drought phenomenon is one of the natural disasters, which may occur in all climatic zones and cause serious damages to the environment and human life. So, forecasting this phenomenon may have significant impact on the water resources management and reduce its destructive effects as much as possible. In this study, the multivariate standardized precipitation index (MSPI) was utilized to compute the drought characteristics in the Lighvanchai basin and then the artificial neural network (ANN) was used to forecast the MSPI values. In order to train the ANN and estimate its optimized weights, the particle swarm optimization (PSO) algorithm was applied and its performance was compared with the backpropagation (BP) algorithm. In this context, different scenarios and structures were considered and then the goodness-of-fit tests were utilized for evaluating the accuracy of them. The results demonstrated that the ANN-PSO model had a better performance than the ANN-BP model for drought forecasting.

One of very important problems in correct managing of groundwater resources is finding potential of this resources to correct planning and deciding for use of them. The purpose of this research is potential evaluation of groundwater resources with the hybrid approach to particle swarm optimization algorithm and geographic information systems in Mehran plain. In this regard and due to evaluation of groundwater resources potential in this area, 13 various factors which have a great impact on level of water permeability in ground and groundwater resources formation Including the slope, height, drainage density, fault density, T map, K map, recharge map, landuse map, lithology map, Sy map, depth of groundwater map, well density map and Cl map, were prepared and classified. Then, by PSO algorithm, each map was assigned weight and with overlay method in GIS combined with each other and at the end 2 final groundwater potential map were obtained, once when that optimization equation equal to the well density map (PSO_chah), and once again when that optimization equation equal to the Sy map (PSO_Sy). In this context, PSO_chah map, 2.56% and PSO_Sy map, 2.40% of area determined as areas with very high potential in case of groundwater resources.

Today, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm Optimization in surface of cucumber field. Population data of pest was obtained in 2017 by sampling in 100 fixed points in a fallow field in Ramhormoz, to evaluate the ability of neural networks combined with Particle Swarm Optimization to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and coefficient determination of linear regression among predicted values and actual values. Results showed that in training and test phases of neural network combined Particle Swarm Optimization algorithm, was no significant effect between variance, mean and statistical distribution of actual values and predicted values. Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field.

Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires finding a set of parameter values of the model which provides thebest fit between observed and estimated hydrographs in a procedure called calibration. Asmanual calibration is tedious, time consuming and requires personal experience, automaticcalibration methods make application of more significant CRR models which are based onusing a systematic search procedure to find good parameter sets in terms of at least oneobjective function.

Conceptual hydrologic models play a significant role inpredicting a basin’s response to different climatic and meteorological processes within natural systems. However, these models require a number of estimated parameters. Model calibration is the procedure of adjusting the parametervalues until the model predictions match the observed data. Manual calibration of high-fidelity hydrologic (simulation) models is tedious, time consuming and sometimesimpractical, especially when the number of parameters islarge. Moreover, the high degrees of nonlinearity involved in different hydrologic processes and non-uniqueness ofinverse-type calibration problems make it difficult to find asingle set of parameter values. In this research, the conceptual HEC-HMS model is integrated with the Particle Swarm Optimization (PSO) algorithm.The HEC-HMS model was developed as areplacement for HEC-1, which has long been considered as astandard model for hydrologic simulation. Most of thehydrologic models employed in HEC-HMS are event-basedmodels simulating a single storm requiring the specificationof all conditions at the beginning of the simulation. The soil moistureaccounting model in the HEC-HMS is the onlycontinuous model that simulates both wet and dry weatherbehavior.Programming of HEC –HMS has been done by MATLAB and techniques such as elite mutation and creating confusion have been used in order to strengthen the algorithm and improve the results. The event-based HEC-HMS model simulatesthe precipitation-runoff process for each set of parameter values generated by PSO. Turbulentand elitism with mutation are also employed to deal with PSO premature convergence. The integrated PSO-HMS model is tested on the Kardeh dam basin located in the Khorasan Razavi province.

Input parameters of hydrologic models are seldomknown with certainty. Therefore, they are not capable ofdescribing the exact hydrologic processes. Input data andstructural uncertainties related to scale and approximationsin system processes are different sources of uncertainty thatmake it difficult to model exact hydrologic phenomena.In automatic calibration, the parameter values dependon the objective function of the search or optimization algorithm.In characterizing a runoff hydrograph, threecharacteristics of time-to-peak, peak of discharge and totalrunoff volume are of the most importance. It is thereforeimportant that we simulate and observe hydrographs matchas much as possible in terms of those characteristics. Calibration was carried out in single objective cases. Model calibration in single-objective approach with regard to the objective function in the event of NASH and RMSE were conducted separately.The results indicated that the capability of the model was calibrated to an acceptable level of events. Continuing calibration results were evaluated by four different criteria.Finally, to validate the model parameters with those obtained from the calibration, tests perfomed indicated poor results. Although, based on the calibration and verification of individual events one event remains, suggesting set is a possible parameter.

All events were evaluated by validations and the results show that the performance model is not desirable. The results emphasized the impossibility of obtaining unique parameters for a basin. This method of solution, because of non-single solutions of calibration, could be helpful as an inverse problem that could limit the number of candidates. The above analysis revealed the existence of differentparameter sets that can altogether simulate verificationevents quite well, which shows the non-uniqueness featureof the calibration problem under study. However, the methodologyhas benefited from that feature by finding newparameter intervals that should be fine-tuned further inorder to decrease input and model prediction uncertainties.The proposed methodology performed well in the automatedcalibration of an event-based hydrologic model;however, the authors are aware of a drawback of the presentedanalysis – this undertakingwas not a completely fair validationprocedure. It is because validation events represent possiblefuture scenarios and thus are not available at the time ofmodel calibration. Hence, an event being selected as a validationevent should not be used to receive any morefeedback for adjusting parameter values and ranges.However,this remark was not fully taken into consideration, mostlybecause of being seriously short of enough observed eventsin this calibration study. Therefore, the proposed methodology,although sound and useful, should be validated inother case studies with more observed flood events.

A DNA string can be supposed a very long string on alphabet with 4 letters. Numerous scientists attempt in decoding of this string. since this string is very long، a shorter section of it that have overlapping on each other will be decoded. There is no information for the right position of these sections on main DNA string. It seems that the shortest string (substring of the main DNA string) is a proper estimation of the main DNA string. Therefore aims of the present study is demonstrating an evolutionary algorithm for selecting the shortest superstring into a DNA string. The practical problem in current study is the shortest superstring problem (SSP). We solve the problem using particle swarm optimization algorithm (PSO) in evolutionary algorithm level by programming language MATLAB version R2011a. In comparison with solving problem by genetic algorithm، the result of present study was much better than the above mentioned algorithm.