الگوریتم تکامل تفاضلی

In this paper, in order to intelligently manage the demand for smart home electrical networks, it includes electrical loads such as refrigerators, air conditioning systems, washing machines, dishwashers, electric ovens, and PHEV that can be charged and discharged by connecting to the city electricity. In this research, the DE algorithm has been used in order to intelligently optimize household load management with the presence of renewable elements and controllable loads. Also, wind and solar power generation sources have been considered, and the uncertainty related to power generation by their annual power generation regime has also been considered. The proposed goal is to reduce electricity costs and also improve the voltage profile of household loads. The load profile considered in this article is real and without averaging, which is adapted from authentic articles. The simulation was implemented by MATLAB software and the results were compared with the basic state in order to validate the results.

Exhaustive testing of software systems with a large number of input parameters and combinations between them often causes the problem of combinatorial explosion. Combinatorial t-way testing is a technique that generates an array of test cases to maximize combinations covering of between input parameters. Generating a minimum covering array is an optimization problem that many strategies based on metaheuristic algorithms such as teaching and learning based optimization, particle swarm optimization, and genetic and cuckoo search algorithms have been used for solving it. Although these strategies have produced smaller covering arrays, complete minimization has not yet been performed. In this paper, we propose a new strategy based on the success-history and linear population size reduction based adaptive differential evolution algorithm (so-called LSHADE), which is one of winners of IEEE CEC competitions, to generate minimum covering array. The results of Friedman mean rank show that the LSHADE strategy has the first rank in terms of generating the covering array with the lowest size and the lowest average number of algorithmic calls, compared to mathematics based strategies such as TConfig, greedy strategies such as IPOG, Jenny and PICT and meta-heuristics such as GS, TLBO, HC-BAT, PSTG, WOA , BAPSO and GSTG. While, in terms of the average number of fitness function evaluations and the average runtime, this strategy has the first rank after the GS strategy. Moreover, the convergence diagrams confirm the high convergence speed of this strategy compared to the other meta-heuristic strategies.

One of the main challenges in speech recognition is noise resistant feature extraction. In this paper, a new feature extraction algorithm, called Fractional and Adaptive Power Normalized Cepstral Coefficients Algorithm, has been proposed as a noise-resistant method for speech recognition. This proposed feature extraction method is based on a fractional short-term Fourier Transform. The selection of fractional conversion coefficient is important for proper analysis of multi-component signals like speech. Therefore, the proposed method obtains the optimum parameter of α for fractional Fourier Transform based on the noise class in the environment, adaptively by the Differential Evolution meta-heuristic algorithm. Moreover, TI Digit and Noisex-92 are used for evaluation of the resistance and accuracy of the recognition of the automatic speech recognition system. Simulation results show more resistance and higher recognition accuracy of the proposed feature extraction method rather than other methods in noisy and without noise environments. In the proposed ASR system, the Support Vector Machine (SVM) classifier with a nonlinear kernel has been used. Also, all the simulations are performed in MATLAB.

With the rapid growth of wind power plants, different types of these resources are connected to the network at different voltage levels. A number of these resources have DFIG. The development of distributed generation has made problems such as an increased level of short circuit and the loss of protective devices coordination. During the fault occurrence, transient stability may be lost due to drastic changes in speed, power, and rotor angel of synchronous and asynchronous generators. Based on the dependence of resistance and temperature, high-temperature superconducting fault current limiter could decrease the fault current. This paper, after studying the ability of HTSFCL to reduce the fault current and to increase the stability level in PSCAD/EMTDC software, for the first time proposes objective function of HTSFCL optimal location by simultaneously considering security and protection coordination indices, voltage, and transient stability of synchronous and asynchronous generators in the networks connected to DFIG. Optimal location and size of HTSFCL in IEEE 30-bus test network are determined using differential evolution (DE) algorithm in MATLAB while considering single- and multi-objective indices. Also, indices of security, coordination of overcurrent relays, voltage stability, and rotor angel stability are compared in six modes. The achieved results show the applicability of the proposed method.

Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requires more investment in constructing additional electrical transmission lines. Power system restructuring and deregulation has increased uncertainties in transmission expansion planning and made investment in electrical transmission lines more complicated and less appealing for private parties. This paper proposes a new approach for transmission line expansion planning in deregulated networks. To do that, a multi objective programming problem which consists of various objective functions such as minimizing capital investment for constructing new transmission lines, minimizing congestion in transmission lines and maximizing the investment from private parties is suggested such that access to competitive, economic and reliable energy market is facilitated. To solve the proposed multi objective optimization problem, the Pareto differential evolution algorithm is used. Applying this algorithm to the proposed multi objective programming problem generates set of optimal plans that shows the best compromise between objective functions. The final plan, among the generated plans, is selected using a max-min fuzzy decision making. The proposed method is applied on the IEEE 24 bus test system and effectiveness of the proposed method is verified.

This study presents a novel intelligent Fuzzy Genetic Differential Evolutionary model for the optimization of a fuzzy expert system applied to heart disease prediction in order to reduce the risk of heart disease. To this end, a fuzzy expert system has been proposed for the prediction of heart disease. The proposed model can be used as a tool to assist physicians. In order to: (1) tune the parameters of the membership function of the fuzzy expert system, (2) improve its performance and (3) increase its accuracy, the Genetic Algorithm combined with the Differential Evolution Algorithm has been applied to the fuzzy expert system. The proposed hybrid models, Fuzzy-GA, Fuzzy-DE, and Fuzzy-GA-DE were evaluated using ROC curve analysis and 10- fold cross-validation methods. In order to evaluate and validate the performance of the model, we applied it to a dataset including 380 samples collected from Parsian Hospital in Karaj. According to the results, the accuracy of the fuzzy expert system was 85.52% that has increased to 97.93% after to apply the hybrid Fuzzy-GA model and has increased to 97.67% after to apply the hybrid Fuzzy-DE model. Moreover, these hybrid models have improved the accuracy of the fuzzy expert system significantly. The ability to interpret the results in the fuzzy expert system and the high accuracy of the hybrid evolutionary models is very promising for the early prediction of heart disease and the provision of necessary care.

Accurate estimation of software service development effort is a great challenge both in industry and for academia. The concept of effort is an important and effective parameter in process development and software service management. The reliable estimation of effort helps the project managers to allocate the resources better and manage cost and time so that the project will be finished in the determined time and budget. One of the most popular effort estimation methods is analogy base estimation (ABE) to compare a service with similar historical cases. Unfortunately ABE is not capable of generating accurate results unless determining weights for service features. Therefore, this paper aims to make an efficient and reliable model through combining ABE method and DE algorithm to estimate the software services development effort. In fact, the DE algorithm was utilized for weighting features in the similarity function of the ABE method. The proposed hybrid model has been evaluated on a real data set and two artificial datasets. The obtained results were compared with common effort estimation methods. Obtained values indicate 28, 34 and 19 percentage improvement on the three datasets ISBSG, Moderate, and Severe, respectively.

Biogeography-Based Optimization (BBO) which is a new population based evolutionary optimization method inspired by biogeography and Differential Evolution (DE) is a fast and robust evolutionary algorithm for optimization problems. DE algorithm is good at the exploration of the search space and finds global minimum but is not good in exploitation of solutions. In this paper، we combine the exploration of DE with the exploitation of BBO to solve multi-objective problems by introducing a hybrid migration operator effectively. The proposed algorithm (MOBBO/DE) makes the use of nondominated sorting approach improve the convergence ability efficiently and hence it can generate the promising candidate solutions. It also combines crowding distance to guarantee the diversity of Pareto optimal solutions. The proposed approach is validated using several test functions and some metrics taken from the standard literature on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.

This paper presents a new method to design a robust power system stabilizer (PSS) by using the self-tuning fuzzy logic (SFL) technique in combination with Differential Evolution Algorithm (DEA). Depending on the process trend، the output Scaling Factor (SF) of the controller is modified on-line by an updating factor (). The DEA based method is used for off-line training of this controller. Simulation results are carried out on a multi-machine power system. Comparative results of the proposed controller (SFLPSS) with conventional fuzzy logic PSS (FLPSS) designed by DEA، lead-lag PSS designed by DEA (DELLPSS) and conventional PSS (CPSS) show the improvement in dynamic performance of the system.