دکتر رامتین معینی

دانشیار

گروه مهندسی عمران
دانشکده عمران و حمل‌و‌نقل

پست الکترونیکی

r.moeini@eng.ui.ac.ir

آدرس

دانشگاه اصفهان میدان آزادی اصفهان، ایران
کد پستی : 8174673441

معرفی کلی

برونداد‌های پژوهشی

همه موارد

مقالات

مرتب سازی بر اساس:

مقالات لاتین

2025

A new promoted Surface Water Supply Index for multi-faceted drought assessment

( Article . )

Klaho, M.H., Alijanian, M., Moeini, R.

Stochastic Environmental Research And Risk Assessment (14363259)39(4)pp. 1605-1621

One major characteristic of the Standardized Drought Indices is their dependence on one variable. However, different variables influence drought simultaneously. To address this issue, this study modifies and develops the Surface Water Supply Index (SWSI) to involve multiple aspects of droughts. Data on precipitation, runoff, reservoir volume, and discharge (instead of snow) over 21 years are used to modify the SWSI in Marun, Khuzestan Province, Iran. Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Reservoir Storage Index (SRSI), and Standardized Discharge Index (SDI) are used as standard univariate indices to evaluate a new comprehensive index. The Shannon Entropy (SE) method is utilized innovatively to determine the weights of the comprehensive index, instead of deriving them depending on the weather experts. Additionally, a Modified-Surface Water Supply Index (M-SWSI) is also proposed to align it with the existing standard indices. For comparison, the weights are also calculated using the Proportioning Objective Procedure (POP) method, and presented in the POP-SWSI index. The results indicate that utilizing the SE method to determine weights provides an exceptional perspective on the meteorological and hydrological conditions of the drought-affected upstream region; while determining the weights using the POP method provides an insightful socio-economic and hydrological view on the drought-affected downstream region. In 2017, one of the most severe years, the M-SWSI index detected a drought event one month earlier than the POP-SWSI, with a drought duration value of 6 months close to the duration value of 5 months identified by POP-SWSI. Furthermore, the severity values were similar between the two indices, although the M-SWSI indicated lower drought severity than the univariate indices. Copulas are also employed for drought events analysis and to build the joint distribution function of drought severity (S) and duration (D) for the M-SWSI. Bivariate cumulative probability distribution functions are created and analyzed to determine the periodic “and” and “or” bivariate drought return periods. Additionally, Severity-Duration-Frequency (SDF) curves are established to evaluate the M-SWSI index. The Marun dam has been chosen as a case study to analyze the surface water supply under drought conditions, aiming to develop management policies for downstream decision-making. Consequently, the M-SWSI can be applied to any dam reservoir for similar analyses. This index involves various aspects of drought and can be utilized for reservoir management, assessing risks, and addressing flood and flood risk management challenges. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

Correlation–based reliability index equipped with machine learning methods to complete the groundwater level gaps

( Article . Gold )

Hosseini, S.H., Moeini, R.

Results in Engineering (25901230)25

Equipping stochastic dynamic programming with copula function to determine reservoir operation rule curves and risk assessment for current and future conditions

( Article . )

Klaho, M.H., Moeini, R., Alijanian, M.

Environment, Development and Sustainability (1387585X)

Graph Theory-Based Algorithm to Define District Metered Areas Considering Hydraulic Conditions

( Article . )

Jazayeri, P., Moeini, R.

Journal of Water Resources Planning and Management - ASCE (07339496)151(10)

Graph theory-based methods are widely used to partition water distribution networks (WDNs) into district metered areas (DMAs). In general, the Girvan-Newman (GN) algorithm partitions networks based solely on the edge betweenness, a purely topological criterion, without considering the intrinsic hydraulic characteristics of WDNs. To address this limitation, in this study, an improved approach, named the improved Girvan-Newman (IGN) algorithm, is proposed, which integrates hydraulic parameters into the partitioning process. Specifically, pressure (as a quantity parameter) and chlorine concentration (as a quality parameter) are incorporated into the algorithm. The IGN method involves modeling the WDN as a graph, simulating it using either the demand-driven simulation method or the head-driven simulation method during a 24-h extended period simulation (EPS), and assigning nodal weights based on hydraulic values. Edge scores are then calculated using these nodal weights, and the edge with the lowest score is iteratively removed, with corresponding links closed in the hydraulic model. Then, the WDN is reanalyzed to update the hydraulic parameters. This iterative process continues until the desired number of DMAs is achieved. Two case studies, a small-scale WDN, named Poulakis, and a large-scale real WDN of Baharestan city in Isfahan, Iran, are used to investigate the performance of the IGN algorithm. In Poulakis WDN, by using IGN, the average nodal pressure decreases by 1.6%, the chlorine concentration increases by 9.1%, and the hybrid reliability improves by 3.7% compared to the standard GN algorithm. In addition, in Baharestan WDN, the related values are 5.1%, 3.4%, and 4.2%, respectively. The IGN method also enables flexible and time-dependent DMA configurations during EPS, in which the final configuration is selected based on a floating index. Here, a novel integration of hydraulic features into the GN algorithm is proposed, offering a more effective and reliable approach to WDN partitioning and management. © 2025 American Society of Civil Engineers.

Maximizing the coverage of quality sensors in the water distribution network (case studies: Lee and Deininger and the second and the third EPANET networks)

( Article . Gold )

Najarzadegan, M.R., Moeini, R.

Results in Engineering (25901230)25

Predicting the urban water demand by equipping intelligent-based methods with discrete wavelet transform function

( Article . Gold )

Alikhani, M.R., Moeini, R.

Applied Water Science (21905495)15(2)

Quality simulation of dam reservoir using GP model (case study: ZayandehRoud dam reservoir)

( Article . )

Varshabi n., , Moeini, R., Mousavizadeh s.r., S.R.

International Journal of Environmental Science and Technology (17351472)22(13)pp. 12307-12316

2024

Cyclic storage approach for conjunctive operation of surface and groundwater systems (Case study: ZarrinehRoud catchment area)

( Article . Green )

Moeini, R., Sarhadi, K.

Soft Computing (14327643)28(3)pp. 1989-2014

In this research, the problem of optimal conjunctive operation of surface and groundwater systems is investigated by proposing a cyclic storage approach. This problem is solved here using mathematical programming and some efficient Meta heuristic algorithms. For this purpose, the mathematical model of this system is defined and first solved by the nonlinear programming (NLP) method. In addition, the performance of the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gravitational search algorithm (GSA) and, particle swarm optimization (PSO) algorithms are also studied to solve this problem. Here, two case studies, meaning a hypothetical benchmark system and conjunctive use of Buchan dam reservoir and Miandoab aquifer located in the catchment area of Urmia Lake (ZarrinehRoud catchment area) as a real problem, are considered to study the performance of proposed methods. In other words, for the hypothetical benchmark system, the results show that the optimal operating cost and related computational time are equal to 5.2428 Billion Rials and 5400 s, respectively, obtained by using the NLP method. In addition, in comparison with the result of the NLP method, the operation costs increased by 26.36%, 26.1%, 44.91% and, 21.28% using ABC, GA, GSA and, PSO algorithms, respectively. However, the computational time is extremely decreased in comparison with the related value of the NLP method using these algorithms for a particular case. In other words, for the real benchmark system, the results show that the optimal operating cost and related computational time are equal to 139.0145 Billion Rials and 259,200 s, respectively, obtained by using the NLP method. In addition, in comparison with the result of the NLP method, the operation costs increased by 43.74%, 32.32%, and 50.57% using ABC, GA and, PSO algorithms, respectively. However, by using this algorithm, the computational time is extremely decreased in comparison with the related value of the NLP method for a particular case. Furthermore, in both case studies, the water demands are fully stratified using the proposed methods. Therefore, the obtained results show the efficiency and effectivity of the proposed methods to solve this complex optimization problem considering a cyclic storage approach. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

District metered areas determination for water distribution networks using improved Girvan-Newman algorithm

( Article . Gold )

Jazayeri, P., Moeini, R.

Ain Shams Engineering Journal (20904479)15(5)

In general, partitioning networks into district metered areas (DMA) is known as a practical method to manage the pressure and reduce water leakage in Water Distribution Networks (WDNs). Different methods such as engineering judgment, optimization techniques, and graph theory are proposed to determine the DMA. The graph theory is a traditional method for partitioning networks including several algorithms. One of these algorithms is the Girvan-Newman (GN) algorithm which is based on the mathematical parameters without considering the characteristics of networks. In other words, the hydraulic quality and quantity condition of WDNs are not used for DMA determination. Therefore, in this research, a new method is proposed to improve the performance of the GN algorithm for DMA determination considering the quantities of water networks. For this purpose, the average values of nodal pressure and residual chlorine concentration are calculated and used simultaneously for the junction's weight determination. Then, the edge scores are calculated based on the junction's weights, and the edges with the maximum scores are removed until to reach the desired number of DMAs. For comparison purposes, here, the Demand-Driven Simulation Method (DDSM) and Head-Driven Simulation Method (HDSM) analyses are used for the analysis of the Poulakis WDN, selected as a case study. A comparison of the results shows that by using the proposed method, the average pressure values are decreased and the average residual chlorine concentration values are increased. In other words, the pressure values are decreased from 16.32% to 26.23% and the average residual chlorine concentration values are increased from 2.5% to 18.37% in comparison with the results of the standard form of the GN algorithm using both DDSM and HDSM analyses. Furthermore, by using the proposed method, in all scenarios, the hybrid reliability values increase in comparison with the GN algorithm. The results indicate the unique performance of the proposed method in comparison with the standard form of the GN algorithm for DMA determination of WDNs. © 2024 THE AUTHORS

Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- NSGA-II Method

( Article . )

Moeini, R., Nasiri, K., Hosseini, S.H.

Water Resources Management (09204741)38(11)pp. 4137-4159

Using the dragonfly algorithm to find the optimal location of quality sensor of Lee and Deininger and Second and Third networks of EPANET software to reduce the contaminated water values

( Article . Gold )

Najarzadegan, M.R., Moeini, R.

Results in Engineering (25901230)23

Accessing reliable quantitative and qualitative drinking water is one of the requirements of human society. Therefore, the water distribution network is designed to distribute reliable drinking water to consumers. In general, the entrance of any unintentional and intentional contamination reduces the reliability of this network. Therefore, it is necessary to monitor the water distribution network using quality sensors to minimize the amount of contaminated water. In this research, three benchmarks of the Lee and Denninger network and the second and third network examples of EPANET software are selected as case studies. Here, by defining different scenarios, the optimal locations of quality sensors are determined using the dragonfly algorithm (DA) and the results are compared with the genetic algorithm (GA). This algorithm is a binary (zero-one) algorithm that has not any hyperparameters leading to reliable results. In addition, this algorithm is not used in the fields of quality monitoring of water distribution networks and therefore it is used here. The results showed the efficiency of the DA to solve this problem. In other words, for the Lee and Deninger network, the amount of contaminated water is reduced by 4.98 %, considering one sensor and consumption pattern one, 26.69 %, considering at least two sensors and consumption pattern one, 27.52 %, considering one sensor and consumption pattern two, and 19.76 %, considering least two sensors and consumption pattern two, using the DA. In addition, for the second network example of EPANET, the amount of contaminated water is reduced by 32.09 %, considering the 24-h consumption pattern and one sensor, and 20.8 %, considering the 24-h consumption pattern and at least two sensors. Finally, for the third network example of EPANET, the amount of contaminated water is reduced by 0.122 %, considering the 24 h consumption pattern and one sensor, and 0.345 %, considering the 24-h consumption pattern and at least two sensors. © 2024 The Authors

2023

Management of aquifer and dam reservoir quantitative-qualitative interaction

( Article . Hybrid Gold )

Mousavizadeh s.r., S.R., Moeini, R., Shanehsazzadeh, A.

Agricultural Water Management (18732283)277

Investigating the quantity and quality interaction of the surface and groundwater resources plays an essential role in the integrated management of the surface and groundwater resources. In this study, to estimate the discharge and nitrate concentration of the interaction between the Chadegan aquifer and the Zayandeh-Roud reservoir, a quantitative-qualitative simulation model is developed using MODFLOW and MT3DMS codes. Here, the model is calibrated using the data from the beginning of winter 2011 to the end of winter 2014, and validated for spring 2014 to summer 2020. For the quantitative calibration, in the steady and unsteady conditions, the RMSE values of the water level are 0.74 m and 1.84 m, respectively, and for the qualitative calibration, the measured nitrate concentration values is 1.89 mg/L. In addition, for the validation state of the proposed model, the values of RMSE for the water levels and nitrate concentrations are 3.76 m and 2.85 mg/L, respectively. The results indicate that the aquifer has always been recharged by the reservoir during this time. In addition, the exchange nitrate concentration value is between 39.56 and 48.85 mg/L. The decreasing trend of the water level and the increase in the nitrate concentration, which exceed the permissible limit in some areas, indicate the necessity of adopting the immediate managerial policies. Five managerial scenarios are proposed and the effect on the aquifer conditions in terms of increase in the water level and the decrease of nitrate concentration is investigated. The results indicate that the 20% gradual reduction of the agricultural water consumption and simultaneously a 20% gradual reduction in nitrate fertilizers consumption and completing the urban wastewater treatment systems is the most effective quantitative-qualitative scenario for this region, with a 0.24% increase in the average water level and a 11.36% decrease in the average concentration of the nitrate in the aquifer by the end of the forecasting period. © 2022 The Authors

Optimal Design of Water Distribution Network Using Improved Artificial Bee Colony Algorithm

( Article . )

Najarzadegan, M.R., Moeini, R.

Iranian Journal Of Science And Technology, Transactions Of Civil Engineering (22286160)47(5)pp. 3123-3136

Pipe failure prediction of wastewater network using genetic programming: Proposing three approaches

( Article . Gold )

Hoseingholi, P., Moeini, R.

Ain Shams Engineering Journal (20904479)14(5)

Spatial Downscaling of Snow Water Equivalent Using Machine Learning Methods Over the Zayandehroud River Basin, Iran

( Article . )

Moradizadeh, M., Alijanian, M., Moeini, R.

PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science (25122819)91(5)pp. 391-404

2022

Hybrid methods for reservoir operation rule curve determination considering uncertain future condition

( Article . )

Moeini, R., Hadiyan, P.P.

Sustainable Computing: Informatics and Systems (22105379)35

In this research, simple and practical methods have been proposed to determine the reservoir operation rule curves considering uncertain water inflow values at future conditions based on the concept of comparative reservoirs management. For this purpose, two methods are proposed. In the first method named His-Inf, by considering the historical water inflow values, a mathematical optimization model is proposed for defining the reservoir operation rule curves determination problem and solved using nonlinear programming (NLP) method. However, in the second method, three different approaches named Pre-Inf1, Pre-Inf2 and Pre-Inf3 are proposed. In the Pre-Inf1 method, at first, water inflow values into the dam reservoir are predicted using neural network (ANN) model. Then, the predicted water inflow values are used to determine and update the reservoir operation rule curves using NLP method. In the Pre-Inf2 method, the ANN model is used to simulate and update the constant coefficients of the reservoir operation rule curves considering the water demand, water inflow values into the reservoir, water release values form reservoir and time index (T) as data set. For this purpose, two different framework and structure are proposed for sorting and providing data set leading to two different cases. Finally, in the Pre-Inf3 method, at first, water inflow values, reservoir storage volumes, water demand values and time index (T) are used to simulate and predict water releases values from the reservoir. Then, the linear regression method is used to determine the reservoir operation rule curves and policies. Here, both static and dynamic ANN models are used for simulating and predicting the water inflow values and reservoir operation rule curves and policies. In order to investigate the performance of proposed methods, here, the Sefidrood dam reservoir is considered as a case study in which it is located at the intersection of the QezelOzan River and Shahroud River near the city of Manjil in the north of Iran. Finally, the obtained results are presented and compared by calculating the reliability, residency, vulnerability and sustainability indexes. Comparison of the result indicates the superiority of the proposed Pre-Inf3 method for determining the reservoir operation rule curves at uncertain future condition in which the results of the His-Inf, Pre-Inf1 and Pre-Inf2 methods are almost similar. In other words, the sustainability index of His-Inf, Pre-Inf1, Pre-Inf2 and Pre-Inf3 are 63%, 63%, 63% and 74%, respectively, in which the corresponding value of Pre-Inf3 is 17.5% bigger than other proposed methods. © 2022 Elsevier Inc.

Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models

( Article . )

Pishgah hadiyan, P., Moeini, R., Ehsanzadeh, E., Karvanpour, M.

Water Resources Management (09204741)36(8)pp. 2703-2723

2021

Hybridizing ANN-NSGA-II model with genetic programming method for reservoir operation rule curve determination (Case study Zayandehroud dam reservoir)

( Article . Green )

Moeini, R., Nasiri, K.

Soft Computing (14327643)25(22)pp. 14081-14108

One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non-dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy method, stochastic dynamic programming, ANN model, and NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best-obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Reduce repair cost of wastewater pipelines for using trenchless and open cut technology

( Article . Gold )

Moeini, R., Zare, M., Karimian f.,

Ain Shams Engineering Journal (20904479)12(1)pp. 423-435

2020

Application of static and dynamic artificial neural networks for forecasting inflow discharges, case study: Sefidroud Dam reservoir

( Article . )

Hadiyan, P.P., Moeini, R., Ehsanzadeh, E.

Sustainable Computing: Informatics and Systems (22105379)27

Construction cost minimisation of the stepped spillway using improved particle swarm optimisation and artificial bee colony algorithms

( Article . )

Jazayeri, P., Moeini, R.

Water and Environment Journal (17476585)34(S1)pp. 468-480

Deriving optimal operation of reservoir proposing improved artiﬁcial bee colony algorithm: Standard and constrained versions

( Article . Bronze )

Soghrati, F., Moeini, R.

Journal of Hydroinformatics (14647141)22(2)pp. 263-280

Extension of the constrained gravitational search algorithm for solving multi-reservoir operation optimization problem

( Article . Bronze )

Moeini, R., Soltani-nezhad m.,

Journal of Environmental Informatics (17262135)36(2)pp. 70-81

Hybrid SVM-CIPSO methods for optimal operation of reservoir considering unknown future condition

( Article . )

Moeini, R., Babaei, M., Moeini, R., Babaei, M.

Applied Soft Computing (15684946)95

In this paper, new hybrid methods have been proposed to solve reservoir operation optimization problem for uncertain water inflows condition by equipping improved particle swarm optimization (IPSO) algorithm with support vector machine (SVM) method. The constrained version of IPSO algorithm (CIPSO) has been used here to improve the efficiency of the IPSO algorithm. In CIPSO algorithm, the problem constraints have been explicitly satisfied leading to smaller search space and finally smaller computational cost. Two approaches have been considered to propose the hybrid methods. In the first approach, named SVM-CIPSO1, water inflows into the dam reservoir have been predicted using SVM model and these predicted values have been used to solve reservoir operation optimization problem using CIPSO algorithm. However, in the second approach, named SVM-CIPSO2, at first, the CIPSO algorithm has been applied to solve reservoir operation optimization problem using the historical data and finally the optimal water release values have been used as input and output data to create a SVM model for predicting optimal water release from reservoir for the future condition. For comparison purpose, the ANN model has been also used to predict the water inflow or release values for the future condition and the standard form of IPSO algorithm has been also used to solve the optimization problem. Here, to evaluate the proposed approaches, the optimal water release values form Zayandehroud dam reservoir have been obtained using proposed methods and the reliability, resiliency, vulnerability and sustainability indexes have been computed. Comparison of the results indicates the capability of the proposed methods to predict the optimal water release values for future condition with acceptable accuracy. In other words, the RMSE values of SVM model for test, validation and training processes are 9.7104 (23. 56196), 11.2553 (42.69093), and 7.9556 (47.9346) MCM, respectively, which are obtained using second (first) approach. In addition, the best reliability, resiliency, vulnerability and sustainability index values are 51.95% (45.45%), 45.95% (38.10%), 3.539 (0.0041) MCM and 62.02% (55.74%), respectively, which are obtained using SVM-CIPSO2 (SVM-CIPSO1) method. © 2020 Elsevier B.V.

Optimal design of cascade spillway using meta-heuristic algorithms: Comparison of four different algorithms

( Article )

Jazayeri, P., Moeini, R.

Environmental Engineering and Management Journal (15829596)19(4)pp. 687-700

Optimum outflow determination of the multi-reservoir system using constrained improved artificial bee colony algorithm

( Article . )

Moeini, R., Soghrati, F.

Soft Computing (14327643)24(14)pp. 10739-10754

2019

Ant intelligent applied to sewer network design optimization problem: Using four different algorithms

( Article . )

Moeini, R.

Environmental Engineering and Management Journal (15829596)18(5)pp. 957-971

Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

( Article . )

Babaei, M., Moeini, R., Ehsanzadeh, E.

Water Resources Management (09204741)33(6)pp. 2203-2218

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

( Article . )

Moeini, R., Afshar, M.H.

Water Environment Research (10614303)91(4)pp. 300-321

The ant colony optimization algorithm (ACOA) is hybridized with nonlinear programming (NLP) for the optimal design of sewer networks. The resulting problem is a highly constrained mixed integer nonlinear problem (MINLP) presenting a challenge even to the modern heuristic search methods. In the proposed hybrid method, The ACOA is used to determine pipe diameters while the NLP is used to determine the pipe slopes of the network by proposing two different formulations. In the first formulation, named ACOA-NLP1, a penalty method is used to satisfy the problem constraints while in the second one, named ACOA-NLP2, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily satisfied as box constraint of the NLP solver leading to a considerable reduction of the search space size. In addition, the assumption of minimum cover depth at the network inlets is used to calculate the nodal cover depths and the pump and drop heights at the network nodes, if required, leading to a complete solution. The total cost of the constructed solution is used as the objective function of the ACOA, guiding the ant toward minimum cost solutions. Proposed hybrid methods are used to solve three test examples, and the results are presented and compared with those produced by a conventional application of ACOA. The results indicate the effectiveness and efficiency of the proposed formulations and in particular the ACOA-NLP2 to optimally solve the sewer network design optimization problems. • Practitioner points • ACOA is hybridized with NLP for the effective optimal design of sewer networks. • Here, ACOA is used to determine pipe diameters and NLP is used to determine the network pipe slopes with predefined pipe diameters. • In ACOA-NLP1, a penalty method is used to enforce the problem constraints. • In ACOA-NLP2, velocity and flow depth constrains are expressed in terms of slope constraint. © 2019 Water Environment Federation.

2018

Different hydraulic analysis conditions for sewer network design optimisation problem using three different evolutionary algorithms

( Article . )

Moeini, R.

International Journal of Operational Research (17457653)33(4)pp. 512-537

Simulation-optimization model for design of water distribution system using ant based algorithms

( Article . Gold • Green )

Moeini, R., Moulaei s.a.m.,

Journal of Engineering Research (17266742)15(1)pp. 42-60

2017

Arc Based Ant Colony Optimization Algorithm for optimal design of gravitational sewer networks

( Article . Gold )

Moeini, R., Afshar, M.H.

Ain Shams Engineering Journal (20904479)8(2)pp. 207-223

Arc based ant colony optimization algorithm for solving sewer network design optimization problem

( Article . Gold )

Moeini, R.

Scientia Iranica (23453605)24(3)pp. 953-965

Constrained gravitational search algorithm for large scale reservoir operation optimization problem

( Article . )

Moeini, R., Soltani-nezhad m., , Daei, M.

Engineering Applications of Artificial Intelligence (09521976)62pp. 222-233

Constrained improved particle swarm optimization algorithm for optimal operation of large scale reservoir: proposing three approaches

( Article . )

Moeini, R., Babaei, M.

Evolving Systems (18686486)8(4)pp. 287-301

2015

Corrigendum: Layout and size optimization of sewer networks by hybridizing the GHCA model with heuristic algorithms (Scientia Iranica (2015) 22: 5 (1742-1754))

( Article )

Rohania m., , Afshara m.h., , Moeini, R.

Scientia Iranica (23453605)22(6)pp. 2069-2069

2013

Constrained Ant Colony Optimisation Algorithm for the layout and size optimisation of sanitary sewer networks

( Article . )

Moeini, R., Afshar, M.H.

Urban Water Journal (17449006)10(3)pp. 154-173

Extension of the Constrained Ant Colony Optimization Algorithms for the optimal operation of multi-reservoir systems

( Article . Bronze )

Moeini, R., Afshar, M.H.

Journal of Hydroinformatics (14647141)15(1)pp. 155-173

Sewer network design Optimization problem using ant Colony Optimization Algorithm and Tree Growing Algorithm

( Article . )

Moeini, R., Afshar, M.H.

Advances in Intelligent Systems and Computing (discontinued) (21945365)227pp. 91-105

2012

Layout and size optimization of sanitary sewer network using intelligent ants

( Article . )

Moeini, R., Afshar, M.H.

Advances in Engineering Software (18735339)51pp. 49-62

Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network

( Article . Gold )

Amani j., , Moeini, R.

Scientia Iranica (23453605)19(2)pp. 242-248

2011

Arc-based constrained ant colony optimisation algorithms for the optimal solution of hydropower reservoir operation problems

( Article . )

Moeini, R., Afshar, M.H.

Canadian Journal of Civil Engineering (03151468)38(7)pp. 811-824

Calibration of water distribution hydraulic models: A comparison between pressure dependent and demand driven analyses

( Article . )

Tabesh, M., Jamasbb m., , Moeini, R.

Urban Water Journal (17449006)8(2)pp. 93-102

Fuzzy rule-based model for hydropower reservoirs operation

( Article . )

Moeini, R., Afshar a., , Afshar, M.H.

International Journal of Electrical Power and Energy Systems (01420615)33(2)pp. 171-178

2009

Application of an ant colony optimization algorithm for optimal operation of reservoirs: A comparative study of three proposed formulations

( Article )

Moeini, R., Afshar, M.H.

Scientia Iranica (23453605)16(4 A)pp. 273-285

2008

Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems

( Article . )

Afshar, M.H., Moeini, R.

Water Resources Management (09204741)22(12)pp. 1835-1857

This paper presents a constrained formulation of the ant colony optimization algorithm (ACOA) for the optimization of large scale reservoir operation problems. ACO algorithms enjoy a unique feature namely incremental solution building capability. In ACO algorithms, each ant is required to make a decision at some points of the search space called decision points. If the constraints of the problem are of explicit type, then ants may be forced to satisfy the constraints when making decisions. This could be done via the provision of a tabu list for each ant at each decision point of the problem. This is very useful when attempting large scale optimization problem as it would lead to a considerable reduction of the search space size. Two different formulations namely partially constrained and fully constrained version of the proposed method are outlined here using Max-Min Ant System for the solution of reservoir operation problems. Two cases of simple and hydropower reservoir operation problems are considered with the storage volumes taken as the decision variables of the problems. In the partially constrained version of the algorithm, knowing the value of the storage volume at an arbitrary decision point, the continuity equation is used to provide a tabu list for the feasible options at the next decision point. The tabu list is designed such that commonly used box constraints for the release and storage volumes are simultaneously satisfied. In the second and fully constrained algorithm, the box constraints of storage volumes at each period are modified prior to the main calculation such that ants will not have any chance of making infeasible decision in the search process. The proposed methods are used to optimally solve the problem of simple and hydropower operation of "Dez" reservoir in Iran and the results are presented and compared with the conventional unconstrained ACO algorithm. The results indicate the ability of the proposed methods to optimally solve large scale reservoir operation problems where the conventional heuristic methods fail to even find a feasible solution. © Springer Science+Business Media B.V. 2008.