Hybrid Genetic Algorithm Method Research Articles

Heuristic-Based Hybrid Algorithm for Value Stream Design with Milk-Run Approach

In this study, a heuristic algorithm has been developed for the Milk-Run related to the vehicle routing problem. It aimed to supply in the right place and at the right time in a short time with the internal logistics system approach. Since the proposed problem formulation is NP-hard, we suggested a heuristic-based hybrid genetic algorithm method to solve the problem. Real life problem is solved with a milk run approach inspired by vehicle routing problems. Firstly the model was developed with mixed integer linear programming then the problem was solved with the proposed hybrid genetic algorithm. The aim reduce the total transportation cost in the network and the number of vehicles required by using an efficient vehicle routing strategy. It explains the change in the existing distribution and collection systems of a logistics service company. The response of variables such as time, weight, volume, and pallet was measured under various scenarios with cost and time savings by applying Milk-Run optimization. The deterministic model and the proposed heuristic algorithm compared the previews and outputs of the paths. Accordingly, 30% and 50% discounts were made on restrictions for six different scenarios.

Identification of Soil Mechanical Parameters by Inverse Analysis Using Stochastic Methods

Abstract The mechanical parameters of the soil that must be introduced into geotechnical calculations, in particular those carried out by the Finite Element Method, are often poorly understood. The search for the numerical values of these parameters so that the models best reflect the observed reality constitutes the inverse analysis approach. In this article, we are interested in the identification of the mechanical parameters of the soil based on the principle of inverse analysis using the two methods of stochastic optimization, the genetic algorithm and the hybrid genetic algorithm with Tabu search. Soil behavior is represented by the constitutive soil Mohr-Coulomb model. The identification relates to the following two parameters: The shear modulus (G) and the friction angle (φ). The validation of these two stochastic optimization methods is done on the experimental sheet pile wall of Hochstetten in Germany. The results obtained by applying the genetic algorithm method and the hybrid genetic algorithm method for the identification of the two Mohr-Coulomb parameters (G, φ) show that the hybridization process of the genetic algorithm combined with the Tabu search method accelerated the convergence of the algorithm to the exact solution of the problem whereas the genetic algorithm alone takes a much longer computation time to reach the exact solution of the problem.

Hybrid Genetic Algorithm for IOMT-Cloud Task Scheduling

Task scheduling for the cloud is one of the main advances in IoMT stage, which impacts the whole execution of the cloud resource. Cloud is a proficient headway for computation, and it encompasses data storage, management, and manipulation in large volumes. Thus, a proposition is being made a better approach to proffer task scheduling in the cloud. In this case, a new hybrid genetic algorithm (HGA) is proposed. The proposed HGA method will be justified by contrasting it with the previous researches and approaches. The CloudSim is utilized to quantify their effect on various metrics like timing factors and resource utilization. The proposed HGA technique enhanced the viability of task scheduling with a better execution rate of 32.57 ms. Thus, the experimented outcomes show that the HGA also reduces cost profoundly.

Evaluation of the hybrid method of genetic algorithm and adaptive neural-fuzzy network (ANFIS) model in predicting the bankruptcy of companies listed on the Tehran stock exchange

One of the most important issues in financial, economic, and accounting matters is the phenomenon of bankruptcy and its prediction. There is presented a hybrid method of genetic algorithm and adaptive neural-fuzzy network model (ANFIS) to evaluate predicting the bankruptcy of companies listed on the Tehran Stock Exchange. The statistical population of this research is the successful and bankrupt manufacturing companies in Tehran Stock Exchange and in this research, there is a different way as opposed to previous and purposeful research and all companies can prevent their possible bankruptcy with accurate forecasting. In this way, the statistical population includes 136 companies consisting of bankrupt and non-bankrupt companies. In order to construct prediction models, four variables were first selected: 1. Independent sample t-test, 2. Correlation matrix (CM), 3. Step-by-step diagnostic analysis (SDA) and 4. Principal component analysis (PCA). The final financial ratios were selected from 19 financial ratios that using selected financial ratios and a hybrid model of ANFIS and genetic algorithm and the results of the proposed model and its comparison with the hybrid model of genetic algorithm and group method of data handling shows the high capability of the proposed GA-ANFIS model in bankruptcy prediction modeling and its superiority over GA-GMDH method. The results also show that the CM-GA-ANFIS model is known as the best model for predicting bankruptcy of companies listed on the Tehran Stock Exchange. The main reason for choosing the model (GA-ANFIS) is that in addition to the fact that for the first time a combination of two methods ANFIS and genetic algorithm is used to predict the bankruptcy of companies, and also in none of the studies conducted in both areas which further highlights the need for the present study.

Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.

A hybrid method of genetic algorithm and support vector machine for DNS tunneling detection

With the expansion of the business over the internet, corporations nowadays are investing numerous amounts of money in the web applications. However, there are different threats could make the corporations vulnerable for potential attacks. One of these threats is harnessing the domain name protocol for passing harmful information, this kind of threats is known as DNS tunneling. As a result, confidential information would be exposed and violated. Several studies have investigated the machine learning in order to propose a detection approach. In their approaches, authors have used different and numerous types of features such as domain length, number of bytes, content, volume of DNS traffic, number of hostnames per domain, geographic location and domain history. Apparently, there is a vital demand to accommodate feature selection task in order to identify the best features. This paper proposes a hybrid method of genetic algorithm feature selection approach with the support vector machine classifier for the sake of identifying the best features that have the ability to optimize the detection of DNS tunneling. To evaluate the proposed method, a benchmark dataset of DNS tunneling has been used. Results showed that the proposed method has outperformed the conventional SVM by achieving 0.946 of f-measure

A hybrid method of genetic algorithm and support vector machine for intrusion detection

With the development of web applications nowadays, intrusions represent a crucial aspect in terms of violating the security policies. Intrusions can be defined as a specific change in the normal behavior of the network operations that intended to violate the security policies of a particular network and affect its performance. Recently, several researchers have examined the capabilities of machine learning techniques in terms of detecting intrusions. One of the important issues behind using the machine learning techniques lies on employing proper set of features. Since the literature has shown diversity of feature types, there is a vital demand to apply a feature selection approach in order to identify the most appropriate features for intrusion detection. This study aims to propose a hybrid method of Genetic Algorithm and Support Vector Machine. GA has been as a feature selection in order to select the best features, while SVM has been used as a classification method to categorize the behavior into normal and intrusion based on the selected features from GA. A benchmark dataset of intrusions (NSS-KDD) has been in the experiment. In addition, the proposed method has been compared with the traditional SVM. Results showed that GA has significantly improved the SVM classification by achieving 0.927 of f-measure.

Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors

Supercapacitor as a clean energy storage device has been widely adopted in powering electric motors of vehicles. Precise evaluation of aging state of supercapacitors, i.e., the remaining useful life provides a feedback to replace damaged cells to sustain the comfort and safety of electric vehicle. Currently reported evaluation methods for such aim are data or model-based predications, which are either time consuming or of low precision. To achieve efficient and robust evaluation of the remaining lifetime, this work proposes a general strategy based on the combination between a recurrent neutral network method, i.e., long short-term memory, and hybrid genetic algorithm. The sequential quadratic programming as a local search operator of the genetic algorithm, enhances its global search ability, which allows quickly search for the local optimal solution in the means of the dropout probability and the number of hidden layer units. Eventually we apply this predication method to supercapacitors charging at steady state mode and succeed in estimating their remaining useful life. Such life prediction approach also gains validity in supercapacitors with dynamic operative cycle. Indeed, high accuracy has been achieved at both the online trained supercapacitors with root mean square errors ranging from 0.0161 and 0.0214, and offline historical data with 0.0264 error. Moreover, the estimation time is shortened to 3550 s, which is shortened by 60%. This evaluation model may pave the way in predicting the remaining useful lifetime of supercapacitors as well as secondary ion batteries in a precise and robust fashion.

Estimation of near-saturated soil hydraulic properties using hybrid genetic algorithm-artificial neural network

Near-saturated hydraulic properties are the key parameters for water transport models in the unsaturated zone and essential for management practices. This study was conducted to compare efficacy of multiple linear regression (MLR) and hybrid method of genetic algorithm with artificial neural network (GA-ANN) for prediction of near-saturated soil hydraulic properties in Moghan plain, north-western Iran. The results of MLR analysis indicated that this method had low potential to predict near-saturated soil hydraulic properties in the study area, which only could explain 14–38% of variability in the studied properties. Otherwise, GA-ANN was much higher powerful that could explain about 35–80% of total variability in the mentioned properties in the study area. The results of sensitivity analysis suggest that soil particle size distribution, organic matter, electrical conductivity and relative bulk density were the most crucial with variety of priorities for explaining variability of the near-saturated soil hydraulic properties in the study area in the semiarid region. In overall, it was concluded that application of intelligent system using the easily available soil properties as predictors could provide reliable estimates of near-saturated soil hydraulic properties at the filed scale.

Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive Patient

The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.

AN EFFICIENT HYBRID MACHINE LEARNING METHOD FOR TIME SERIES STOCK MARKET FORECASTING

Time series forecasting like stock price predicting is one of the most important complications in the financial area with non-stationary and highly-noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method,the output values are further fed to a developed ANN algorithm to fix the error on exact point. Our analysis suggests that the GA and ANN can increase the accuracy in less iteration.We analyzed the 200-day main index as well as five of the companies listed on the NASDAQ. By applying the proposed method to the Apple stock dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms; we reach to improvement in SSE and timeimprovement to traditional methods. These results show these performances and the speed and the accuracy of our proposed approach.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

Abstract A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Comparative Assessment of the Hybrid Genetic Algorithm–Artificial Neural Network and Genetic Programming Methods for the Prediction of Longitudinal Velocity Field around a Single Straight Groyne

In the present paper, three-dimensional flow fields around single straight groynes with various lengths have been discussed. The dataset of the flow field is measured in the laboratory using Acoustic Doppler Velocimeter (ADV). Then, the longitudinal velocity field is modelled using a novel hybrid method of Genetic Algorithm based artificial neural network (GAA) that has the ability to automatically adjust the number of hidden neurons. To investigate the proposed method’s performance, the results of GAA is measured and compared with one of the most common genetic algorithm based prediction method, namely genetic programming (GP). It is concluded that that GAA model successfully simulates the complex velocity field, and both the velocity magnitudes and isovel shapes are well predicted by this model. The results show that GAA with RMSE of 0.1236 in test data has a significantly better performance than the GP model with RMSE of 0.2342. In addition, it was founded that the transverse coordinate of the measuring point (Y*) is the most important input variable.

Estimating NDMA Formation in a Distribution System Using a Hybrid Genetic Algorithm

A model was developed for estimation of N‐nitrosodimethylamine (NDMA) concentration in a case study of a chloraminated water distribution system in Australia using influencing parameters that had an impact on NDMA formation via a hybrid genetic algorithm method. The model was based on hydraulic retention time, temperature, and monochloramine residual. The genetic algorithm and quasi‐Newton optimization with a Broyden–Fletcher–Goldfarb–Shanno Hessian updating method were combined into a hybrid parameter optimization scheme for robust calibration of NDMA model parameters. In order to test the applicability and the accuracy of the proposed model, NDMA concentration and water quality characteristics for 220 post‐disinfection water samples collected between 2006 and 2016 were used. Good agreement between measured and estimated NDMA concentrations was observed by performing statistical analysis on an independent set of water sample data (R2 = 0.93). This modeling approach has the potential to be used as a decision support tool in order to manage and improve the chloramine disinfection process.

A multi-machine multi-product EPQ problem for an imperfect manufacturing system considering utilization and allocation decisions

This paper considers a multi-product problem with non-identical machines. This manufacturing system consists of various machine types with different production capacities, production costs, setup times, production rates and failure rates. One of the major issues in the planning phase of a manufacturing system is to take the best decision about which machines must be utilized to manufacture which items. As a result, the decision makers face three critical questions: what machines must be purchased, which items should be allocated to each machine, and what is the optimal cycle length. These decisions must be made to minimize system costs including utilization, setup, production, holding and scrap costs. The multi-machine multi-product economic production quantity (EPQ) problem for an imperfect manufacturing system is formulated as a mixed integer non-linear programming (MINLP), where the convexity property of multi-product single machine EPQ model is used to convert the problem into a bi-level decision-making problem. In the first level, decisions about machine utilization and items allocation are made. After, in the second level the optimal cycle length for each machine is determined. To solve the problem at hand, a hybrid genetic algorithm (HGA) is proposed integrating genetic algorithm and derivatives method. In the proposed HGA, the solutions of the first level are obtained randomly and then, for the second level, the derivatives method is applied to obtain optimal cycle length based on solutions of the first level. Finally, the results of HGA method are compared to the results of general algebraic modeling system (GAMS) and it is found that HGA method has better and more efficient results. Also, a numerical experimentation and a sensitivity analysis of the model are done.

A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm.

Design-Informatics Approach for Intimate Configuration of Silent Supersonic Technology Demonstrator

The intimate configuration of the silent supersonic technology demonstrator has been designed using the design-informatics approach. As a first step, multidisciplinary design optimization with multi-objectives has been performed for the wing shape of a silent supersonic technology demonstrator among aerodynamics, structures, aeroelasticity, and boom noise. Aerodynamic evaluation was carried out by solving Euler equations on computational fluid dynamics, and composite structural weight evaluation was performed by using MSC. NASTRAN for strength and flutter requirements on computational structural dynamics. The intensity of sonic boom was evaluated by a modified linear theory. The optimization problem had four objective functions as the minimizations of the pressure drag and the boom intensity at supersonic condition, and the composite structural weight. The intimate configuration defined by 50 design variables was optimized on particle swarm optimization and genetic algorithm hybrid method. In the structural evaluation, the combination optimization of stacking sequences of laminated composites was performed for inboard and outboard wings with strength and flutter requirements. Consequently, 37 non-dominated solutions were obtained. As a second step, data mining has been performed to obtain the design knowledge for deciding a compromise solution. The data mining revealed the knowledge in the design space, such as the tradeoff information among the objective functions, and the correlations between objective functions and design variables. A compromise solution was successfully determined by using the obtained design knowledge. Design-informatics approach is essential for an efficient design process.

Design of Optimization Parameters with Hybrid Genetic Algorithm Method in Multi-Cavity Injection Molding Process

This paper combines an artificial neural network (ANN) with a traditional genetic algorithm (GA) method, called hybrid genetic algorithm (HGA), to analyze the warpage of multi-cavity plastic injection molding parts. Simulation results indicate that the minimum and the maximum warpage of the hybrid genetic algorithm (HGA) method were lower than that of the traditional GA method and CAE simulation. These results reveal that, when HGA is applied to multi-cavity plastic warpage analysis, the optimal process conditions are significantly better than those using the traditional GA method or CAE simulation.

Improved power quality in a single-phase PWM inverter voltage with bipolar notches through the hybrid genetic algorithms

Inverters are such electronic devices that are extensively used to convert DC power to AC power in wide-range domestic and industrial applications. DC power is usually supplied from either national grid through the rectifiers or batteries for high and low energy demands, respectively. When they are employed to feed electrical devices the efficiency of these devices tends to decrease due to harmonic occurrence in them. One solution to this problem is the selective harmonic elimination which is required to solve multiple non-linear equations simultaneously. These equations are mainly function of the switching angles and are obtained from a type of inverter output voltage such as being unipolar, bipolar, etc. To eliminate one or more harmonics in these output voltages becomes a typical multivariate root-finding problem and its solution requires root-finding methods such as the Newton–Raphson method, the Broyden method etc. These methods can only produce a meaningful solution if initial values are properly guessed, but it is highly difficult task due to having no direct formula. In this paper, the hybrid genetic algorithm method based on the refinement of the genetic algorithms results through the Newton–Raphson method was used to simultaneously solve the non-linear equations. The results have shown a considerable effect on decrease in the total harmonic distortion, thereby increasing the efficiency of devices compared to conventional methods under the same conditions.