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## Hybrid Job Scheduling Algorithm for Cloud Computing Environment

### Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 (2014-01-01) 303: 43-52 , January 01, 2014

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).

## A Pi-Sigma Higher Order Neural Network for Stock Index Forecasting

### Computational Intelligence in Data Mining - Volume 2 (2015-01-01) 32: 311-319 , January 01, 2015

Multilayer perceptron (MLP) has been found to be most frequently used model for stock market forecasting. MLP is characterized with black-box 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 Pi-Sigma 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).

## Minimization of reliability indices and cost of power distribution systems in urban areas using an efficient hybrid meta-heuristic algorithm

### Soft Computing (2017-10-04): 1-25 , October 04, 2017

Power distribution systems (PDS) in urban areas suffer from different types of problems. One such major problem is accidental or scheduled interruption. In electrical networks, effects of interruptions are usually quantified using a set of reliability indices, namely the System Average Interruption Frequency Index and the System Average Interruption Duration Index. Installation cost (fixed cost) and cost due to temporary and/or permanent faults during interruptions (variable cost) are also major issues to be considered while achieving a cost efficient, fault-tolerant PDS. Formalization of an optimization problem that jointly minimizes the afore-mentioned reliability indices as well as the cost of a PDS by optimal allocation of different protective devices and switches has always been a challenging task. This paper presents a hybrid single as well as joint-objective function optimization technique to minimize different reliability indices (mixed integer minimization problems), as well as the operational cost of a PDS in urban areas. In the proposed technique, two well-known meta-heuristic search techniques, namely genetic algorithms (GA) and ant colony optimization (ACO), have been hybridized after modifying different participating operators. The effectiveness of the proposed algorithm is examined, and each PDS is tested in a different environment of constrained optimization. In addition, the presented simulation results are compared with existing approaches that solve this problem. The simulation results show the superiority of the proposed hybrid GA–ACO model, as compared to other established heuristic approaches.

## Optimization of soil moisture sensor placement for a PV-powered drip irrigation system using a genetic algorithm and artificial neural network

### Electrical Engineering (2017-03-01) 99: 407-419 , March 01, 2017

The efficiency and installation costs of solar-powered 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 solar-powered 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 $$6\,\hbox {m} \times 6\,\hbox {m}$$ micro-regions. 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 micro-region using only five soil moisture sensors in a non-homogeneous area. Thus, daily energy and water consumption were decreased by 32 %, while the moisture rate in the soil was maintained within the desired range.

## Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm

### The International Journal of Advanced Manufacturing Technology (2014-03-01) 71: 365-376 , March 01, 2014

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in real-world 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.

## Genetic Algorithm-Based Heuristic for Solving Target Coverage Problem in Wireless Sensor Networks

### Advanced Computing and Communication Technologies (2018-01-01) 562: 257-264 , January 01, 2018

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 algorithm-based 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.

## Application of Response Surface Methodology and Genetic Algorithm for Optimization and Determination of Iron in Food Samples by Dispersive Liquid–Liquid Microextraction Coupled UV–Visible Spectrophotometry

### Arabian Journal for Science and Engineering (2017-10-24): 1-12 , October 24, 2017

A simple and facile method was developed for the determination of trace amount of iron. The method is based on the complex formation between Fe (III) and picrate anion in the presence of piroxicam, as a complexing agent. Dispersive liquid–liquid microextraction (DLLME) was applied to extract the formed ion associate, Fe (III)-piroxicam. The absorbance of the extracted iron in the sedimented phase was measured by UV–Vis spectrophotometry. Two statistical methods of response surface methodology and genetic algorithm (GA) based on artificial neural network (ANN) were employed for prediction and optimization of a four-constituent DLLME. Plackett–Burman design was used for screening the influential parameters including pH, the volume of picrate anion, disperser, and extraction solvents. Central composite design (CCD) was used to obtain the optimum levels in the proposed method. The experimentally obtained data were used to train the GA model. CCD and GA models were compared for their predictive abilities. The result showed that both models have the ability to predict the proposed process, but ANN model is more reliable than CCD. The absorbance of the extracted iron obeys Beer’s law in the range of 0.03–0.96 $$\upmu \hbox {g}\,\hbox {mL}^{-1 }({R}^{2} = 0.998)$$ , and the limit of detection of 0.008 $$\upmu \hbox {g}\ \hbox {mL}^{-1}$$ and enhancement factor of 88.84 were achieved for the process. The developed procedure was successfully applied to the determination of iron in water samples and two types of common vegetation sample, i.e., tea and mint.

## Design of Closed-Loop Supply Chain Model with Efficient Operation Strategy

### Proceedings of the Eleventh International Conference on Management Science and Engineering Management (2018-01-01): 962-974 , January 01, 2018

In this paper, a closed-loop supply chain model with efficient operation strategy (CLSC-OS) is proposed. In the CLSC-OS, 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 CLSC-OS 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 revenue-cost rate is considered and analyzed. Experimental results shows that discount rate and profit premium rate have a significant influence on revenue-cost rate.

## Analysis of maintenance cost for an asset using the genetic algorithm

### International Journal of System Assurance Engineering and Management (2017-06-01) 8: 445-457 , June 01, 2017

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 in-turn depend upon the scheduled maintenance interval. Thus, a trade-off 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.

## A Feature Selection Algorithm for Big Data Based on Genetic Algorithm

### Recent Developments in Mechatronics and Intelligent Robotics (2018-01-01) 690: 159-163 , January 01, 2018

Features selection is an important task since it has significant impact on the data mining performance. This paper present an algorithm to perform feature selection based on the adaptive genetic algorithm. First, the method to compute the crossover probability and mutation probability were proposed. Therefore, the subset feature selection operation can be seen as a process of evolution, and realized adaptive feature subsets selection and optimization. Experimental results demonstrate that the proposed algorithm achieves notable classification accuracy improvements and reduced the total computing time compare to the conventional algorithm.