جستجوی مقالات مرتبط با کلیدواژه « metaheuristics » در نشریات گروه « برق »

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.

Recently there has been a surge in the usage of metaheuristic algorithms to design materials with optimum performance. In this article, one such recently proposed metaheuristic algorithm called RPSOLC (Repulsive Particle Swarm Optimization with Local search and Chaotic perturbation) has been used to design diamond-like carbon (DLC) thin films having better hardness. Based on a Box-Behnken design, 15 independent experiments on DLC deposition are conducted in a PECVD (plasma-enhanced chemical vapor deposition) setup by varying the CH4-Argon flow rate, hydrogen flow rate and the deposition temperature. The nano-hardness of the DLCs are evaluated using nano-indention tests. The hardness is then expressed as the function of the three process parameters using a polynomial regression metamodel. Finally, the metamodel is optimized using RPSOLC and compared with optimal predictions of a traditional GA. It is seen that RPSOLC has faster convergence and is more reliable than the GA. In general, a high H2 flow rate along with low CH4-Ar flow rate and high temperature is found to be beneficial in improving the hardness.

Stock markets have a key role in the economic situation of the countries. Thus one of the major methods of flourishing the economy can be getting people to invest their money in the stock market. For this purpose, reducing the risk of investment can persuade people to trust the market and invest. Hence, Productive tools for predicting the future of the stock market have an undeniable effect on investors and traders’ profit. In this research, a two-stage method has been introduced to predict the next week's index value of the market, and the Tehran Stock Exchange Market has been selected as a case study. In the first stage of the proposed method, a novel clustering method has been used to divide the data points of the training dataset into different groups and in the second phase for each cluster’s data, a hybrid regression method (HHO-SVR) has been trained to detect the patterns hidden in each group. For unknown samples, after determining their cluster, the corresponding trained regression model estimates the target value. In the hybrid regression method, HHO is hired to select the best feature subset and also to tune the parameters of SVR. The experimental results show the high accuracy of the proposed method in predicting the market index value of the next week. Also, the comparisons made with other metaheuristics indicate the superiority of HHO over other metaheuristics in solving such a hard and complex optimization problem. Using the historical information of the last 20 days, our method has achieved 99% accuracy in predicting the market index of the next 7 days while PSO, MVO, GSA, IPO, linear regression and fine-tuned SVR has achieved 67%, 98%, 38%, 4%, 5.6% and 98 % accuracy respectively. in this research we have tried to forecast the market index of the next m (from 1 to 7) days using the historical data of the past n (from 10 to 100) days. The experiments showed that increasing the number of days (n), used to create the dataset, will not necessarily improve the performance of the method.

The recent state of electrical system comprises the conventional generating units along with the sources of renewable energy. The suggested article recommends a method for the solution of single and multi-objective optimal power flow, incorporating wind energy with traditional coal-based generating stations. In this article, the two thermal power plants are replaced with the wind power plants. The techno-economic analysis are done with this state of electrical system. In proposed work, Weibull probability distribution functions is used for calculating wind power output. A non-dominated sorting based multi-objective moth flame optimization technique is used for the optimization issue. The fuzzy decision-making approach is applied for extracting the best compromise solution. The results are authenticated though modified IEEE-30 bus test system, which is combined with wind and thermal generating plants.

This paper presents an optimal approach to design Fractional-Order Digital Integrators (FODIs) using a metaheuristic technique, called Hybrid Flower Pollination Algorithm (HFPA). HFPA is a hybrid approach which combines the exploitation and exploration capabilities of two dierent evolutionary optimization algorithms, namely, Particle Swarm Optimization (PSO) and Flower Pollination Algorithm (FPA). The proposed HFPA based designs are compared with the designs based on Real Coded Genetic Algorithm (RGA), PSO, Dierential Evolution (DE), and FPA. Simulation results demonstrate that HFPA based FODIs of all the dierent orders consistently achieve the best magnitude responses. The proposed technique yields FODIs which surpass all the designs based on both classical and evolutionary optimization approaches reported in recent literature.

Hub location problem (HLP) has been an attractive area of research for more than four decades. A recently proposed problem in the area of hub location is the hierarchical single-allocation hub median problem (SA-H-MP) which is associated with finding the location of a number of hubs and central hubs, so that the total routing cost is minimized. Owing to the problem’s complexity and intractability, this paper puts forward two metaheuristics, simulated annealing (SA) and iterated local search (ILS), and compares their performances. Results show that while both algorithms are able to reach optimal solutions on the standard CAB dataset, their runtimes are negligible and considerably lower compared to the runtimes of exact methods.

An accelerated firefly algorithm (AFA) for fast size optimization of truss structures is proposed in this paper. Metaheuristic firefly algorithm has been recently developed and its effectiveness in solving practical problems such as sizing optimization of truss structures has not been thoroughly explored. The numerical experiments show that although the standard firefly algorithm (FA) is a powerful approach for truss optimization, it suffers from slow rate of convergence, and hence it should be modified to solve real-life problems. The proposed AFA imposes some improvements on the searching procedure by both reduction of randomness and scaling the random term in fireflies'' motion. The effectiveness and robustness of the algorithm are investigated by solving some benchmark problems. The results revealed that the proposed AFA remarkably enhances the rate of convergence and stability of standard firefly algorithm.