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Javanmardi, Saeed; Shojafar, Mohammad; Amendola, Danilo; Cordeschi, Nicola; Liu, Hongbo; Abraham, Ajith
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15 Citations
In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).
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Nayak, S. C.; Misra, B. B.; Behera, H. S.
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1 Citations
Multilayer perceptron (MLP) has been found to be most frequently used model for stock market forecasting. MLP is characterized with blackbox in nature and lack of providing a formal method of deriving ultimate structure of the model. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities that require less memory in terms of weights and nodes and have been utilized in many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper considered a HONN, called PiSigma Neural Network (PSNN), for prediction of closing prices of five real stock markets. The tunable weights are optimized by Gradient Descent (GD) and a global search technique, Genetic Algorithm (GA). The model proves its superiority when trained with GA in terms of Average Percentage of Errors (APE).
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Zhou, Pingzhang; Du, Jianbin; Lü, Zhenhua
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4 Citations
A vibration isolation system is designed using novel hybrid optimization techniques, where locations of machines, locations of isolators and layout of supporting structure are all taken as design variables. Instead of conventional parametric optimization model, the 01 programming model is established to optimize the locations of machines and isolators so that the timeconsuming remeshing procedure and the complicated sensitivity analysis with respect to position parameters can be circumvented. The 01 sequence for position design variables is treated as binary bits so as to reduce the actual number of design variables to a great extent. This way the 01 programming can be solved in a quite efficient manner using a special version of genetic algorithm(GA) that has been published by the authors. The layout of supporting structure is optimized using SIMP based topology optimization method, where the fictitious elemental densities are taken as design variables ranging from 0 to 1. Influence of different design variables is firstly investigated by numerical examples. Then a hybrid multilevel optimization method is proposed and implemented to simultaneously take all design variables into account.
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Dursun, Mahir; Özden, Semih
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2 Citations
The efficiency and installation costs of solarpowered drip irrigation systems depend on not only the efficiencies of the electrical motor, its driver, and the pump, but also the efficient usage of irrigation water. In this study, the initial installation costs and energy consumption of photovoltaic irrigation systems were decreased by obtaining the soil moisture level as a reference for optimizing energy and water consumption in a solarpowered drip irrigation system. The data from 15 moisture sensors placed in the area covered by the system were collected by a central unit using radio transmission. The soil moisture was estimated via an artificial neural network with the data obtained for
$$6\,\hbox {m} \times 6\,\hbox {m}$$
microregions. Next, the locations of the moisture sensors in the area were optimized using a genetic algorithm to provide the optimum energy and water consumption in the system. Subsequently, the drip irrigation was controlled using moisture data from only five sensors located at the best points, as determined by the genetic algorithm. The obtained experimental results indicated that the moisture rate at the end of the period of irrigation using the system developed was more homogeneous than that of traditional irrigation systems for each microregion using only five soil moisture sensors in a nonhomogeneous area. Thus, daily energy and water consumption were decreased by 32 %, while the moisture rate in the soil was maintained within the desired range.
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Pasandideh, Seyed Hamid Reza; Niaki, Seyed Taghi Akhavan; Hemmati Far, Mohammad
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8 Citations
The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a singlevendor singlebuyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in realworld environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time.
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Manju; Singh, Deepti; Chand, Satish; Kumar, Bijendra
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Target coverage problem in the wireless sensor networks schedule sensors into subsets such that all the targets are monitored by each subset. Existing heuristics to solve this problem aims to maximize the total network lifetime. In last few decades, genetic algorithmbased methods have been proved more suitable to solve such optimization problems. In this paper, we propose a solution heuristic for target coverage problem which is based on genetic algorithm approach. The simulation results show that the proposed heuristic outperforms the existing methods.
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By
Chen, Xing; Chuluunsukh, Anudari; Yun, YoungSu; Gen, Mitsuo
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In this paper, a closedloop supply chain model with efficient operation strategy (CLSCOS) is proposed. In the CLSCOS, various facilities which can be used in forward logistics (FL) and reverse logistics (RL) are taken into consideration. A mathematical formulation is proposed to design the CLSCOS and it is implemented using genetic algorithm (GA) approach. In numerical experiment, for efficient operation strategy, several scenarios using various factors such as remanufacturing rate, profit premium rate, discount rate, and return rate based on revenuecost rate is considered and analyzed. Experimental results shows that discount rate and profit premium rate have a significant influence on revenuecost rate.
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By
Salehi Mir, Mir Saber; Rezaeian, Javad; Mohamadian, Hossein
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This study investigates an identical parallel machine scheduling problem with pastsequencedependent setup times and general effects of deteriorating and learning. The actual job processing time on each machine is defined by a twoelement function of the normal processing times of the preprocessed jobs and its scheduled position on the same machine. Moreover, the job setup time on each machine is a function of the actual processing times of the preprocessed jobs on the same machine. A novel mixedinteger programming model is developed to satisfy the goal of minimizing total completion time. Due to the NPhard characteristic and intractability of the problem, three efficient methodologies including a heuristic algorithm (HA), a genetic algorithm (GA) with an enhanced exploration ability and an ant colony optimization (ACO) combined with a new stochastic elitism strategy are designed to find optimal/nearoptimal solutions within an appropriate period of time. The effectiveness and efficiency of the presented model and the proposed algorithms are verified by computational experiments. The computational results indicate that the suggested algorithms are effective and executable approaches to generate solutions as good as optimal solution in the smallsized problems. Also, the ACO statistically outperformed the HA and GA in the medium and largesized problems.
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By
Asjad, Mohammad; Khan, Shahbaz
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3 Citations
Nowadays, almost every firm focuses to beat the global competition across the worldwide. In order to deal with such situation, companies are undertaking efforts to improve the productivity of their products but at the minimum possible cost. Asset management is one of the ways to enhance the productivity under cost constraint which may also be seen as the management strategy for different the phases of asset life cycle. Operations and maintenance is one of the important phases of asset life cycle that can be focussed to improve the productivity. This phase may extend the equipment life, improves availability and retains them in healthy positions. But at the same time, frequent maintenance actions may increase the maintenance cost thereby increase the life cycle cost of a product. The maintenance cost only includes the preventive and corrective maintenance cost and which may inturn depend upon the scheduled maintenance interval. Thus, a tradeoff between maintenance actions and operational objectives (i.e. availability, etc.) is required to minimize the maintenance cost. In this paper, the genetic algorithm is applied to optimize the maintenance cost for higher performance (i.e. availability). A case study is taken into consideration for implementing the GA to optimize the objective function. The three different cases are presented, in the first case, subassemblies are repaired during maintenance action(s); in the second case subassemblies are repaired in preventive maintenance action and while replaced in corrective maintenance action; in the last case, the subassemblies are replaced in both kind of maintenance. In order to check the robustness of the solution, the sensitivity analysis is also performs and that validates the strength of the solution methodology.
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By
Ding, Shuang; Wu, ChihPing
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A hybrid genetic algorithm with the complex method is developed for the optimization of the material composition of a multilayered functionally graded material plate with temperaturedependent material properties in order to minimize the thermal stresses induced in the plate when it is subjected to steadystate thermal loads. In the formulation, the plate is artificially divided into an n_{l}layered plate, and a weakformbased finite layer method is developed to obtain the displacement and stress components induced in the n_{l}layered plate using the Reissner mixed variational theorem. Two thermal conditions, namely the specified temperature and heat convection conditions, imposed on the top and bottom surfaces of the plate are considered. The throughthickness distributions of the volume fractions of the constituents are assumed as certain specific/nonspecific function distributions, such as powerlaw, sigmoid, layerwise step and layerwise linear function distributions, and the effective material properties of the plate are estimated using the Mori–Tanaka scheme. Comparisons with regard to the minimization for the peak values of the stress ratios induced in the FGM plates with various optimal material compositions are conducted.
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