The optimized portfolio formation is one of the important decisions making for corporations, accordingly a portfolio selection with top efficiency rate and controlled risk is one of problems that scholars attend them. In this research, we submit a method with multi objectives genetic algorithm based to portfolio formation and we lionize the value at risk as a paragon for risk measuring. Also we use 50 top companies’ data of stock exchange in time period from 1385 to 1389.The results show that multi objectives genetic algorithm can used to optimized portfolio formation and designed portfolio operation via genetic algorithm is different from with 50 top companies operation with equal weights.

In [1] a feasible timetable generator stochastic simulation modeling framework for the train scheduling problem was developed to obtain a train timetable which includes train arrival and departure times at all visited stations and calculated average train travel time for all trains in the system. In this study, the framework is integrated with a genetic algorithm (GA) in order to get an optimal or suboptimal feasible train timetable with minimum average train travel time.

Bone drilling process is one of the most common processes in the orthopedic surgeries and bone break treatments. It is also very frequent in dentistry and bone sampling operations. Bone is a complex material and the machining process itself is sensitive, so bone drilling is one of the most important, common and sensitive processes in Biomedical Engineering field. Orthopedic surgeries can be improved using robotic bone drilling systems and mechatronic bone drilling tools. In the present study, multiobjective optimization is performed on the temperature and trust force at two steps. At the first step, two regression models are developed for modeling the temperature and force in bone drilling process considering three design variables, namely tool’ s rotational speed (V), feed rate (f) and tool diameter (D). At the second step, using the regression models, multi-objective genetic algorithm is used for the Pareto based optimization of bone drilling process considering two conflicting objectives: temperature and force. It has been found out that there are considerable connections and feasible principles for an optimal design of the process in case of applying Pareto-based multi-objective optimization; otherwise, these interesting results would not be discernible.

Improving the efficiency and suction capability of a multistage centrifugal pump poses a major challenge for the designer of this type of equipment. This paper deals with the optimization of a two stage centrifugal pump using Non-dominated Sorting Genetic Algorithm II (NSGA-II), coupled with three-dimensional Reynoldsaveraged Navier-Stokes (3D-RANS) flow solver. The first stage comprises a suction impeller with a diffuser while the second stage is formed by a second impeller connected to a volute. Both impellers are of different dimensions and are inter-connected by a return channel. This arrangement increases the number of varying parameters and thus can add further constraints on the overall optimization process; as a result, a high computational complexity of NSGA-II and a higher computational fluid dynamics (CFD) simulation cost is incurred. In order to save running time, optimization with CFD simulations are performed on each stage separately shall enable to obtain better parameterization flexibility; therefore, permitting to adopt only three objective functions in as well as limiting other geometrical constraints. The objectives of this study are to maximize the head and hydraulic efficiency at a time where the net positive suction head inception (NPSHi) is kept to minimum. The overall efficiency as well as the head of the optimized pump were increased by 9. 8% and 15. 7%, respectively, at best efficiency point (BEP) (rotational speed N=2600 rpm); the NPSHi of suction impeller was reduced by 13. 6%. At N=1450 rpm (BEP), an improvement of 14. 9% in the head and 6. 52% for the overall efficiency is observed. An important improvement in performance at different operating flow rates was obtained; this was in addition to other enhancements in the volumetric and hydraulic efficiencies. Unsteady CFD simulations were also performed to predict fluctuations in the pressure field, leakage flows and interactions between impellers and collectors. The obtained results were in agreement with experimental data. The head fluctuation of the optimized pump was also reduced by 22. 5% in amplitude; this was favored by the presence of a tapered blade towards the trailing edge and the extended radial gap by 4. 86% between the second impeller and cutwater, which was caused by the reduction of the impeller diameter.

In this paper, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based approach is presented for distribution system reconfiguration. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. In order to illustrate the performance of the proposed method, 33-bus and 69-bus distribution networks have been employed which have led to the desired results.

Mathematical simulation of groundwater resource systems is one of the essential tools in managing these valuable resources and calibration of the groundwater simulation models is the time-consuming, and complicated step of these systems. Automated calibration, developed in recent years by researchers with different algorithms, is one of the effective methods to overcome these computational problems. On the other hand, lack of field data in terms of time and space and the hydrological and hydrogeological complexities leads to many uncertainties in the calibration results. The SUFI-II algorithm is an uncertainty-based automatic calibration method that is capable of calibration and uncertainty analysis of numerical simulation models. In this paper, for the first time, this algorithm is used to calibrate and analyze the uncertainty of hydrodynamic parameters (hydraulic conductivity and specific yield) of the MODFLOW model. The results of model implementation for the Ardabil plain groundwater model (Northwestern Iran), indicate an average of 62 percent of the observation data within the 95 percent confidence interval. Finally, the best intervals of parameters are determined for the hydraulic conductivity and specific yield by the proposed approach. Also, the calibration of the groundwater model has been carried out using PEST. According to the results, the root-mean-squared error (RMSE) value in this case (RMSE = 3. 37) is higher than the SUFI-II method (RMSE = 1. 86), which indicates better performance of the SUFI-II algorithm than the PEST model.

This paper deals with the optimal set of directional overcurrent relays using intelligent methods when the problem has multi-objective functions with different constraints. Due to the linear and nonlinear constraints in the coordination of the directional overcurrent relays, the method used is based on Pareto Envelope-based Selection Algorithm to obtain optimal values of plug setting, time multiplier setting, and relay characteristic curve by considering all governing constraints setting parameters of the network primary and backup relays. In this study, there is a simultaneous time reduction between the performance of the main and backup relays. Besides, in this method, while solving a multi-objective function without the need for weight factors, a penalty function is used to check the correct coordination of the primary and backup relays. The proposed method is implemented in two different standard networks, i. e. 3-bus and modified IEEE 14-bus networks. The results show the efficiency of the method used and the optimality of the values compared to other algorithms.