In the rapidly evolving landscape of web services, efficient interaction and optimal selection among services with different quality parameters are essential. This paper addresses the complex challenge of selecting candidate services for abstract services within probabilistic graph structures. We propose a new hybrid method that combines node-based and path-based graph simplification techniques, allowing for the identification of new patterns, such as parallel and nested loops. We use NSGAII to improve scalability and accuracy in service selection. The proposed approach simplifies the composition graph while optimizing the selection process by considering important quality parameters such as execution cost, response time, and availability. Through systematic simplification and a robust fitness function, we ensure a definitive and accurate response to user queries. The results show significant improvements in the proposed approach compared to existing methods, making it a comprehensive solution for effectively composing web services in dynamic environments.

In this study a new and robust procedure is presented to solve synthesis of isothermal reactor networks (RNs) which considers more than one objective function. This method uses non-dominated sorting genetic Algorithm II (NSGAII) to produce structural modification coupled with quasi linear programming (LP) method for handling continuous variables. The quasi LP consists of an LP by adding a search loop to find the best reactor conversions as well as split and recycle ratios which are much easier to solve. To prevent complexity and ensure optimum solution, only ideal continuous stirred tank reactors (CSTRs), plug flow reactors (PFRs) and PFR with recycle stream are considered in producing reactor networks. Also, to avoid differential equations which appear in design equations of PFR reactors, CSTRs in series are replaced for each PFR. Results show that the proposed method finds better solutions than those reported in the literature.

Studying the bedrock geometry in mining and oil exploration operations to obtain its 2D pattern requires nonlinear reverse computations. Local optimization methods for solving nonlinear inverse problems are based on linearizing the changes of the model similar to a primary model and finding an objective function of minimum error from the parameters of the model; however, these optimization methods are not able to select a suitable primary function that is close enough to the general optimal value. That is to say, every objective function can have several minimum and maximum solutions. The lowest minimum is called the global minimum while the rest of them are named local minima. Therefore, in local inverse methods, the goal is to find the minimum of an objective function, and also an objective function might have a few local minima with different values. In this case, it is not suitable to use gradient-based methods for exploration purposes, unless the primary model is very close to the actual answer, which is outside the control of geological structures or the geometry of the subsurface. Despite the easy execution and high convergence rate of the local methods, there is the possibility of being trapped in local minima because these methods are dependent on the primary model, and also finding more than one optimized point in 2D or 3D simulations; this is why local optimization methods are considered deterministic Algorithms. Multi-objective metaheuristic optimization Algorithms are capable of searching the feasible region and they also provide a solution independent of the primary model. Searching the feasible region means finding all the feasible solutions for a problem. Each point in this region is representing a solution that can be ranked based on its value. One of the important differences between local optimization and metaheuristic methods is constraining. Constraining metaheuristic global optimization methods are only used for constraining the feasible region based on previous knowledge or estimation relations, which is different from constraining local optimization that is used for stabilizing inverse simulation. The Algorithm used in the present work includes non-dominated sorting genetic Algorithm (NSGA-II). The NSGA-II is commonly used to solve problems with multiple, typically conflicting objective functions. This Algorithm is capable of being developed and also has a high potential for solving unbounded multi-objective problems. In the present study, NSGA-II Algorithm was verified and validated using the data produced by an imaginary and complex synthetic model. In the present research work, a hybrid technique of NSGA-II and TOPSIS Algorithms was introduced and utilized as a viable search method for nonlinear modeling of the gravity data, and a substitute for the optimization methods. In order for a more precise examination of the performance of this Algorithm, the imaginary synthetic data were used both with no noise and with up to 10% Gaussian white noise (GWN). Based on the gravimetric data of the Moghan basin and Atacama Desert, Chile, the results obtained from Algorithm indicated good performance of the NSGA-II and NSGAII-TOPSIS Algorithms.

The main problem of water resources planning is the inappropriate allocation between different consumers. Water allocation planning is a complex, multi-variable, and multi-constraint problem, which requires advanced optimization methods to be solved. Classical optimization methods are facing some limitations such as being trapped in local optimum points, and difficulties in handling different variables. In this paper two of these methods including particle swarm optimization, PSO and multiobjective non-dominated sorting genetic Algorithms, NGGAII were explored and their efficiency in optimization water reservoir operation problems is compared. Dealing with the necessary of multiobjective programing accuracy, two single objective models was developed separately using PSO to verify the NSGAII results.

The fluctuation of stock returns in the capital market in different periods is significant and the choice of the study period is very important in solving the stock portfolio optimization problem. hence, in this study, we solved the stock portfolio optimization problem based on the mean-variance model and the mean-percentage of Value at Risk model, which examines the VaR criterion from another perspective. for this purpose, we selected three different study periods in 1393-1398, 1396-98, and 1391-98 in Tehran Stock Exchange as study periods, and used NSGA II and MOABC Algorithms. Then while analyzing different periods in terms of the desirability of the proposed portfolios and data quality, we selected the most efficient period. In order to ensure the results, we compared the proposed solutions resulting from solving the studied models were analyzed using the Algorithms used in each of the studied periods, independently and also a fixed period. The results of this study indicate that the selection of the study period has a significant effect on the quality of the proposed solutions resulting from solving the portfolio optimization problem. The longer the study period and the less fluctuating the average and variance of stock returns of the companies under review in the said period, the more reliable the selected period is and the higher the desirability of solving the stock portfolio optimization problem.

Conceptual rainfall-runoff (RR) models, aiming at predicting stream flow from the knowledge of precipitation over a catchment, evapotranspiration, tempreture, and topography of the basin, have become basic and effective tools for flow regime simulation. Calibration of RR models, e. g. WetSpa which has been developed in Belgium, is a process in which parameter adjustment are made so as to match the dynamic behaviour of the RR model to the observed behaviour of the catchment. This research presents an application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) for multi-objective calibration of WetSpa in Karoon river basin, Iran to optimize 11 global parameters of the WetSpa model. The objective functions are Nash– Sutcliffe and logarithmic Nash– Sutcliffe efficiencies in order to improve the model's performance. Results showed that the evolutionary NSGA-II and PSO Algorithms are capable of locating optimal parameter sets in the search space. The measured correlation coefficient in the calibration process was 0. 69 and 0. 71 for the NSGA-II and PSO Algorithms, respectively. Moreover RMSE values were calculated as 119. 8 and 152. 3 m3/s for the Algorithms. The WetSpa model then was applied for a period of 1-year flood simulation in the basin and the results were analysed. Finally a sensitivity analysis was conducted on the global parameters in which the surface runoff coefficient was the most sensitive parameter with more than 40% influence on the results.

AbstractIntroduction Surface runoff is considered as one of the main components of the hydrological cycle and one of the important sources of water supply. In today's era, with the increasing trend of urbanization and as a result of changing the use of permeable surfaces to impervious surfaces, there have been adverse changes in the quality and quantity of surface runoff. However, by applying flood management methods, this resource can be used in a controlled manner and in the best possible way to meet water needs. Based on this, in recent years, a concept called the best management solutions with the abbreviation BMPs has been proposed to control the quantity and quality of runoff. In these activities, by increasing the retention time of the flood in the reservoirs, increasing the roughness coefficient and increasing the permeability of the surfaces, an attempt is made to reduce the peak discharge and the volume of runoff, as well as control the concentration of pollutants in the runoff.Materials and Methods Based on this and considering the importance of runoff management in a metropolis like Tehran, in this research, a part of the catchment area of the 22nd district of Tehran municipality was selected and evaluated the effects of BMPs on the amount of runoff using mathematical models of precipitation and runoff. In order to investigate the subject of the research, it has been tried by considering three objective functions of runoff quality (including BOD5 and TSS quality parameters), runoff quantity (including the volume of runoff produced in each sub-basin) and cost (including flood damage and maintenance costs of BMPs) to The comparison of two optimization models NSGAII and MOPSO should be paid.Results and DiscussionThe results of these two multi-objective evolutionary optimization Algorithms conclude that the NSGAII optimization Algorithm is more suitable due to the use of features such as crowding distance and the speed of performing different steps in the optimization Algorithm. In addition, the use of MOPSO optimization Algorithm will be easier due to the inclusion of fewer parameters than NSGAII. It is also necessary to mention that reaching the steady state in NSGAII will take place in fewer generations than MOPSO. . Also, the results of the evaluation of BMPs in the form of different scenarios showed that the application of these solutions can reduce the peak discharge from 16.3% to 1.50% and also reduce the volume of runoff from 9.2% to 37.4% depending on the type and The number of BMPs used at the basin level. Considering that, in general, the phenomenon of rainfall-runoff is a process that is strongly influenced by uncertain factors, and the inappropriate selection of design parameters leads to the incorrect estimation of the flood discharge and as a result, the selection of unfavorable dimensions for structures and technical performance becomes inappropriate or uneconomical. Designs and ultimately financial and human losses will be many. Therefore, the correct selection of design parameters is very important. In this regard, better results can be achieved by applying methods such as uncertainty analysis of inputs and effective parameters on the results of modeling or analyzing the sensitivity of the model to changing parameters. Among the input factors of rainfall-runoff models that have a noticeable effect on the results, we can mention the temporal and spatial distribution of rainfall, the continuity of rainfall and the previous soil moisture conditions., it is shown that the MOPSO model produces higher quality solutions in most cases compared to the NSGA-II model. Additionally, in cases where the NSGA-II model provides higher quality solutions, the execution time or distribution of solutions in the MOPSO model is better. This is while in a real-world problem and depending on the type of objective functions and their application, the results obtained from the application of these Algorithms may be contrary to the experimental function results.ConclusionIn this research, after analyzing the uncertainty of the temporal and spatial distribution of rainfall as well as the initial moisture of the soil using the Monte Carlo simulation method and analyzing the sensitivity of the flood hydrograph to the continuation of the rainfall, flood management strategies in the region were investigated. The results of the investigations showed that the highest peak flow is obtained from rainfall with a duration of 0.5 hours, in this case the range of peak flow changes is equal to 34.8 cubic meters per second, which indicates the presence of high uncertainty in the input parameters of the rainfall-runoff model. Overall, in terms of comparing the capabilities of the NSGA-II and MOPSO optimization models in this simulation-optimization problem, it should be noted that the optimal values related to objective functions on the optimal exchange curve by the NSGA-II Algorithm exhibit more dispersion compared to the MOPSO Algorithm, indicating a wider range of scenarios generated by the NSGA-II Algorithm. In fact, using this Algorithm can provide decision-makers with more scenarios with significant diversity in objective function values. This is not observed in the results obtained from the MOPSO Algorithm.

The advent of Internet of Things (IoT) technology has led to the concept of the smart city, in which smart devices are recognized as a necessity. The applications installed on these devices generate large volumes of data that often require real-time processing. However, these devices have limited capabilities and are not capable of processing large amounts of data. Moving all this data to cloud data centers results in higher bandwidth usage, latency, cost and energy consumption. Therefore, providing services to delay-sensitive smart city applications in the cloud is a challenging issue, and meeting the requirements of these applications requires the use of a hybrid cloud and fog paradigm. Fog computing as a complement to the cloud allows data to be processed near smart devices. However, the resources in the fog layer are heterogeneous and have different capabilities, hence, appropriate scheduling of these resources is of great importance. In this paper, the problem of task scheduling for the smart city applications in the cloud-fog environment has been addressed. To this purpose, the task scheduling problem has been modeled as a multi-objective optimization problem, which aims to minimize service delay and energy consumption of the system under deadline constraint. Then, in order to solve this problem and achieve an appropriate scheduling strategy, non-dominated sorting genetic Algorithm II (NSGA-II) with customized operators has been applied. In addition, in order to improve the diversity of the population and the convergence speed of the proposed Algorithm, a combination of chaotic map and opposition-based learning methods have been used to generate the initial population. Also, the approach based on the penalty function has been employed to penalize the solutions that do not meet the deadline constraint. The simulation results reveal that the proposed scheduling Algorithm, compared to its best competitor, improves service response delay, waiting time, execution delay and system energy consumption by 1. 49%, 1. 70%, 2. 7% and 1. 86%, respectively. Furthermore, by properly assigning tasks to the computing nodes, compared to the best competitor, the percentage of missed-deadline tasks is reduced by 1. 89%.