جستجوی مقالات مرتبط با کلیدواژه « multi-objective optimization » در نشریات گروه « برق »

Data security and privacy in data centers is an important issue. The major anxiety in security and privacy is the result of the fact that the topography of important operas can be available to sensitive information. To improve storage in distributed cloud data centers, the Particle Swarm Optimization (PSO) algorithm has been used to copy data between data centers. This paper summarizes the objectives and constraints of the cloud storage problem in order to achieve good performance by considering the shortest data transmission distance, obtaining optimal storage in distributed data centers with reliability based on PSO algorithm between two central data sets. And then it provides an intelligent cryptographic approach that cloud service operators cannot directly access partial data. Numerical results show that the proposed method can provide a good cloud storage strategy  when the number of distributed data centers is equal, the defense of the main threats in the clouds can be done effectively.

In this paper, a stochastic multi-objective optimization model for planning distribution networks has been proposed. After running this model, the optimal configuration will be obtained. The proposed model has two main objectives (1) Minimizing the Net Present Value and (2) Minimizing the Expected Energy Not Supplied. In this model, based on the optimal solution, the network’s buses and feeders can be either AC or DC. Monte Carlo Simulation (MCS) has been used in order to model stochastic variations of load demands and distributed generations’ output. For obtaining the best solution, the planning problem will be solved with NSGA, and then a tradeoff between NPV and EENS will be calculated with Pareto Front. The proposed model will be tested on a 14-Bus network, which includes AC and DC load points, and DGs with AC or DC output. The test network will be planned as a pure AC network, and also as a hybrid AC/DC network for (1) an urban zone and (2) a case in which some of the load points are industrial loads and have a very large interruption cost. Finally, at the end of the paper, some results such as NPV, ENS, network’s loss, and a reliability index such as SAIDI will be compared.

Today, with the increase in data production volume, attention to machine learning algorithms to extract knowledge from raw data has increased. Raw data usually has redundant or irrelevant features that affect the performance of learning algorithms. Feature selection algorithms are used to improve efficiency and reduce the computational cost of machine learning algorithms. A variety of methods for selecting features are provided. Among the feature selection methods are evolutionary algorithms that have been considered because of their global optimization power. Many evolutionary algorithms have been proposed to solve the feature selection problem, most of which have focused on the target space. The problem space can also provide vital information for solving the feature selection problem. Since evolutionary algorithms suffer from the pain of not leaving the local optimal point, it is necessary to provide an effective mechanism for leaving the local optimal point. This paper uses the PSO evolutionary algorithm with a multi-objective function. In the proposed algorithm, a new mutation method that uses the particle feature score is proposed along with elitism to exit the local optimal points. The proposed algorithm is tested on different datasets and examined with existing algorithms. The simulation results show that the proposed method has an error reduction of 20%, 11%, 85%, and 7% in the Isolet, Musk, Madelon, and Arrhythmia datasets, respectively, compared to the new RFPSOFS method.

Quantum computing is a new method of information processing that is based on the concepts of quantum mechanics and leads to strange and powerful events in the quantum field. The logic synthesis of quantum circuits refers to the process of converting a given quantum gate into a set of gates that can be implemented in quantum technologies. The most famous logic synthesis methods are CSD and QSD. The main goal of this study is to present a multi-objective logical synthesis method combining the above two methods in the quantum circuit model with the aim of optimizing the evaluation criteria. In this proposed method, the solution space is created from different combinations of CSD and QSD decomposition methods. The created solution space is a space with a very large exponential size. Then, using a bottom-up approach of multi-objective dynamic programming, a method is presented to search only a part of the entire solution space to find circuits with the optimal Pareto costs. The obtained results show that this method creates a balance between the evaluation criteria and produces many optimal Pareto solutions that can be selected according to different quantum technologies.

This paper deals with the dynamic economic and emission dispatch during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security.

In this study, a new multi-objective method is proposed to optimal manage the local energy resources and customers’ load in a microgrid in the presence of demand response programs. The wind turbine and photovoltaic panel are the renewable energy sources of the microgrid. The time of use and critical peak pricing programs are also used to improve the consumption pattern of customers. Moreover, consumers try to use electrical energy when renewable sources are available. Maximizing the profit of microgrid and minimizing the pollutant gases of the microgrid are the objective functions of the demand side management problem. The multi-objective ant lion optimizer algorithm is used to optimize the indices of the microgrid and create the Pareto front. Then, the fuzzy method is utilized to select the best particle equal to the optimal management plan of the microgrid. Ultimately, the proposed method is evaluated in a sample microgrid. The results demonstrate the high efficiency of the proposed method in improving the performance of the microgrid by optimal managing the local energy sources and customers’ load. The proposed method of energy management increases the profitability of the distribution company and decreases the environmental pollution of the microgrid.