Genetic Algorithm Formulation Research Articles

Optimization of governor parameters for transient process of hydropower system with two turbine units sharing a super long headrace tunnel

AbstractThe governor parameters affect the stability and regulation quality of hydropower system with two turbine units sharing a super long headrace tunnel (SLHT). To improve the transient process of hydropower system, this study investigates the optimization of governor parameters. First, the model of hydropower system with two turbine units sharing an SLHT is established. Second, the optimization scheme of governor parameters for transient process of hydropower system is designed by genetic algorithm. The mathematical formulation of genetic algorithm is illuminated. The implementation procedure of genetic algorithm is illustrated. Then, the optimization results of governor parameters by genetic algorithm are analyzed by illustrating the regulation quality of hydropower system under two operating conditions. Finally, the effect of asymmetrical factors on optimal governor parameters is revealed. The asymmetrical layout of bifurcated penstocks, asymmetrical layout of turbine units, and asymmetrical load disturbances are considered. The results indicate that the objective function of hydropower system is composed of the dynamic indexes for frequency oscillations of two turbine units and water level oscillation in surge tank. The fitness function evolves in the direction that makes the regulation quality of hydropower system become better. Under the symmetrical or asymmetrical bifurcated penstocks, turbine units, and load disturbances, the optimal parameters of governors are asymmetrical. The regulation quality of hydropower system under the governor parameters of genetic algorithm is obviously better than that under the governor parameters of Stein formula. The optimal governor parameters are directly affected by the asymmetrical factors of hydropower system. With the increase of the flow inertia of one bifurcated penstock, the optimal parameters of the other governor become greater.

A-priori modelling of selected physico-chemical properties of Malbec V. Vinifera specie Alphonse Lavallee variety of black grape juice

Estimation of physico-chemical properties is essential for food processing operations ranging from equipment design to plant installation and extending towards packaging, storage and distribution applications. Among the various physico-chemical properties, density, heat capacity and thermal conductivity play an influential role. Herein, the physico-chemical properties such as estimation of density, heat capacity and thermal conductivity for Malbec V. Vinifera specie Alphonse Lavallee variety of black grape juice are estimated as a function of temperature (274 to 339 K) and concentration (13.6–45°Brix). A group contribution method approach coupled with the genetic algorithm (GA) formulation is employed to estimate the parameters and respective non-linear equations for important property parameter calculations are generated. The objective functions use experimental dataset of density, thermal conductivity and heat capacity over the same temperature and concentration range subtracted from the estimated dataset to minimize the objective function values in the genetic algorithm framework. Additionally, cubic equation of state is employed as constitutive relation for such estimation. The results follow a similar trendline and show a close resemblance in comparison of the experimental dataset with high accuracy indicating a potential use of similar methodology towards estimation of properties for other food matrices.Graphical

Heterogeneous WEC array optimization using the Hidden Genes Genetic Algorithm

Wave Energy Converters (WEC) are deployed in arrays to improve the overall quality of the delivered power to the grid and reduce the cost of power production by minimizing the cost of design, deployments, mooring, maintenance, and other associated costs. WEC arrays often contain devices of identical dimensions and modes of operation. The devices are deployed in close proximity, usually having destructive inter-device hydrodynamic interactions. However, in this work, we explore optimizing the number of devices in the array and concurrently, the dimensions of the individual devices (heterogeneous) to achieve better performance compared to an array of identical devices (homogeneous) with comparable overall submerged volume. A techno-economic objective function is formulated to measure the performance of the array while accounting for the volume of material used by the arrays. The power from the array is computed using a time-domain array dynamic model and an optimal constrained control. The hydrodynamic coefficients are computed using a semi-analytical method to enable computationally efficient optimization. The Hidden Gene Genetic Algorithm (HGGA) formulation is used in this optimization problem. During the optimization, tags are assigned to genes to determine whether they are active or hidden. An active gene simulates an active WEC device in the heterogeneous array, while the hidden gene results in a reduction in the total number of devices in the array compared with the homogeneous array. The volume of the heterogeneous array is constrained to be close to that of the homogeneous array. These hidden tags do not exclude the associated devices from the optimization process; these devices keep evolving with the active devices as they might become active in subsequent generations. Heterogeneous arrays were found to perform better than homogeneous arrays.

Ionic liquid promoted extraction of gold(III) from electronic waste: a modeling study

ABSTRACT Being an expensive metal, the large-scale requirement of gold in the ever-growing market of electronics increases the overall cost of manufacturing. Hence, recycling gold from electronic waste mitigates the demand and subsequently bounds the price of electronic goods. However, the extraction of gold from electronic waste poses a severe hazard to the environment. Because of tuneable and fascinating properties, ionic liquids (ILs) have emerged as state-of-the-art solvents to extract gold from electronic waste. Herein, the modeling of IL-assisted liquid-liquid-extraction incorporating UNIQUAC extended Pitzer–Debye–Hückel* (PDH*) thermodynamic model has been used for the extraction process of gold from an aqueous acidic solution. The long-range, weak electrostatic interactions present in ionic liquids are adequately represented by the extended PDH* model. The model parameters are obtained from the polarizable continuum medium (PCM) calculation, conductor-like screening model for real solvent (COSMO-RS), and genetic algorithm formulation. Altogether, 11 hydrophobic ILs consisting of 9 imidazolium, 1 pyrrolidinium, and 1 ammonium class with 51 tie line data points are considered for the modeling. The estimated root mean square deviation of 0.002–0.99% over 51 tie lines reveals the goodness of the fitting. The projected percentage extraction efficiency lies in the range of 98.5% to 99.3%.

Minimum Weight Design of Transversely Stiffened Plate Girder Using Genetic Algorithm

Fundamental advantage of using transversely stiffened plate girders (TSPG) rather than plane web plate girders is in achieving increased strength and stiffness of the web panel. As these girders are made of steel plates, they can be custom designed to suit consumer's requirements. Weight minimization of TSPG by satisfying strength and serviceability conditions, further enables in achieving higher strength to weight ratios. The utilisation of these girders in the building sector in India is growing, and optimising these girders can lead to more cost-effective and structurally sound designs. No significant research has been carried out in the field of optimization of plate girders based on specifications according to IS 800 (2007): General Construction in Steel - Code of Practice (Bureau of Indian Standards). Therefore, in the present study, the main goal is to develop a formulation for optimum weight design of TSPG using genetic algorithm (GA) with weight minimization as objective function. The breadth of flange (bf), thickness of flange (t_f), depth of web (d), thickness of web (t_w), breadth of stiffener (b_s), thickness of stiffener (t_s) and spacing between stiffeners(c) are adopted as the design variables. The optimum girder weight is compared with weight obtained through conventional design using IS 800 (2007). The results demonstrate that conventional design overestimates the girder weight by about 30% on an average and therefore the developed formulation helps in attaining economical girder weight. The viability of the GA formulation is verified by comparing the optimized results with existing experimental and numerical data from literature. Parametric studies are also carried out to understand the behaviour of design variables. Results from these studies show that deeper web reduces flange thickness but requires thicker web in order to ensure ample shear capacity and to prevent shear buckling.

A genetic algorithm approach to design job rotation schedules ensuring homogeneity and diversity of exposure in the automotive industry

Job rotation is a work organization strategy with increasing popularity, given its benefits for workers and companies, especially those working with manufacturing. This study proposes a formulation to help the team leader in an assembly line of the automotive industry to achieve job rotation schedules based on three major criteria: improve diversity, ensure homogeneity, and thus reduce exposure level. The formulation relied on a genetic algorithm, that took into consideration the biomechanical risk factors (EAWS), workers’ qualifications, and the organizational aspects of the assembly line. Moreover, the job rotation plan formulated by the genetic algorithm formulation was compared with the solution provided by the team leader in a real life-environment. The formulation proved to be a reliable solution to design job rotation plans for increasing diversity, decreasing exposure, and balancing homogeneity within workers, achieving better results in all of the outcomes when compared with the job rotation schedules created by the team leader. Additionally, this solution was less time-consuming for the team leader than a manual implementation. This study provides a much-needed solution to the job rotation issue in the manufacturing industry, with the genetic algorithm taking less time and showing better results than the job rotations created by the team leaders.

Novel Module-Based Design Algorithm for Intensified Membrane Reactor Systems

The growing interest in intensified process units that improve efficiency by combining several phenomena into one unit, has led to a loss in degrees of freedom when addressing the control scheme of these units. Previous work demonstrated that a novel module-based design approach to membrane reactors could improve the operability index of membrane reactor systems. This approach sought to decouple the phenomena to regain some degrees of freedom for the control system. However, the computational time to determine such an optimal module design made this class of design problems intractable to solve in a reasonable amount of time. This work proposes a set of design heuristics for a new module-based design approach for membrane reactors. These heuristics are used in combination with a genetic algorithm formulation to produce a novel, two-staged algorithm for the design and control of membrane reactor systems. This algorithm is developed in Python and uses rigorous membrane reactor models built in AVEVA Process Simulation. The proposed algorithm solves the original non-polynomial (NP) complexity problem in polynomial time (P), while still being able to find the optimal designs discovered in previous work through exhaustive methods.

Anisotropic diffusion filtering through multi-objective optimization

Images are often corrupted by noise during their process of acquisition, storage and transmission. This alteration usually deteriorates the perception quality of the image. The central challenge in image denoising corresponds to eliminating the corrupted information while it is maintained the fine features of the original image. Anisotropic Diffusion (AD) is considered a well-established scheme for removing noise in digital images without deteriorating their edges. The selection of parameters that define the AD operation presents a very decisive implication in the filtering results. In spite of the importance of the AD operation, the problem of automatically calculating its parameter according to the image requirements has been scarcely considered in the literature. In this paper, a new multi-objective methodology to obtain the AD parameters for effective filter results is presented. In the proposed approach, the appropriate AD parameter values are obtained as the solution that involves the best possible trade-off between two conflicting scenarios: To minimize the noise content in the image while the contrast is maximized. Both objectives cannot be individually improved without worsening another. The proposed scheme uses to solve this formulation of the Non-dominated sorting genetic algorithm based on reference points (NSGA-III), which represent one of the most robust and powerful algorithms for multi-objective optimization. Then, the final solution is obtained from an analysis of the Optimal Pareto front. Experimental results demonstrate that the proposed method presents better results than existing filtering algorithms in terms of visual quality and standard performance metrics.

Smart Electric Grids Three-Phase Automatic Load Balancing Applications using Genetic Algorithms

Smart power grid is going to be the future grid. Conventional, renewable and alternate sources incorporating for power generation[1]. Smart Electrical Grids require nowadays a large interest in the electrical load distribution balancing problem. This problem is a well known for not having an optimal solution for large-scale systems, where the number of single phase consumers connected to three phase systems increases especially in very large-scale electrical distribution systems. This paper presents a new control technique for an automatic circuit phase change as well as an optimisation approach using Genetic Algorithms (GA) used to enhance the solution of electrical load distribution balancing problem. In the first part of the paper, the system under study is introduced, as well as the various solutions adopted. In the second part of the paper, a GA formulation and implementation of the solution is presented. The efficiency of the GA solution is also discussed.

Voltammetric e-Tongue Based on a Single Sensor and Variable Selection for the Classification of Teas

This work presents a simple and low-cost strategy to obtain voltammetric tongues by using a single voltammetric sensor aided by variable selection techniques. A usual electronic tongue consists of an array of physical sensors followed by data compression before pattern recognition modeling. Alternatively, a single voltammetric sensor can also act as an array of pseudo-sensors—applied potentials in a voltammogram—that can be properly selected to perform a given discrimination. The applicability of this strategy was evaluated in the discrimination of teas. Teas prepared for immediate intake (simulating a home-made tea cup) were analyzed with staircase voltammetry at an epoxy-graphite electrode. Voltammograms were submitted to variable selection previously to linear discriminant analysis (LDA) to identify the applied potentials that improve discrimination of teas according to variety, country of origin, and manufacturer. Successive projections algorithm (SPA), genetic algorithm (GA), and stepwise (SW) formulation were the variable selection techniques evaluated. Best results were achieved with SPA/LDA models, with correct classification rates for the prediction set close to 100%.

A Hybrid Water Distribution Networks Design Optimization Method Based on a Search Space Reduction Approach and a Genetic Algorithm

This work presents a new approach to increase the efficiency of the heuristics methods applied to the optimal design of water distribution systems. The approach is based on reducing the search space by bounding the diameters that can be used for every network pipe. To reduce the search space, two opposite extreme flow distribution scenarios are analyzed and velocity restrictions to the pipe flow are then applied. The first scenario produces the most uniform flow distribution in the network. The opposite scenario is represented by the network with the maximum flow accumulation. Both extreme flow distributions are calculated by solving a quadratic programming problem, which is a very robust and efficient procedure. This approach has been coupled to a Genetic Algorithm (GA). The GA has an integer coding scheme and variable number of alleles depending on the number of diameters comprised within the velocity restrictions. The methodology has been applied to several benchmark networks and its performance has been compared to a classic GA formulation with a non-bounded search space. It considerably reduced the search space and provided a much faster and more accurate convergence than the GA formulation. This approach can also be coupled to other metaheuristics.

Space mapping optimisation of 2D array elements arrangement to reduce the radar cross‐scattering

The authors propose a novel aggressive space mapping (ASM) algorithm for the two-dimensional antenna array elements arrangement to reduce the antenna array radar cross-scattering; meanwhile, to ensure the radiation performance. The coarse model is evaluated with genetic algorithm and analytic formulations, the fine model verification is computed with the full wave method of moments (MoMs) enhanced by the equivalence principle algorithm (EPA). The high-order basis functions and matrix compression technique are employed in the EPA-MoM for further accelerations. Only small number of iterations in the ASM optimisation is required for the fine model verification, to achieve the optimisation requirements. Numerical tests of the horn antenna array optimisation with moderate time are presented to show the efficiency of the proposed algorithm.

Mutation‐based compact genetic algorithm for spectroscopy variable selection in determining protein concentration in wheat grain

Wheat is the third most produced grain in the world after maize and rice. Determining the protein concentration in wheat grain is one of the major challenges for measuring its industrial quality. Samples of wheat can be collected using a spectrophotometer device. The challenge is to associate the energy absorbed by the device with the protein concentration in wheat. The device measures hundreds of variable intensities that can be related to the physicochemical properties. The selection of a subset of uncorrelated variables has been shown to be fundamental for establishing correct correlations and reducing prediction error. A new formulation of a compact genetic algorithm that uses only a mutation operator is proposed. The results produced by the proposed approach are compared with traditional techniques for spectroscopy variable selection as successive projection algorithms, partial least square and classical formulations of genetic algorithms. For near-infrared spectral analysis of the protein concentration in wheat, the prediction errors decreased from 0.28 to 0.10 on average, a reduction of 63%.

A multi-objective genetic optimization for spectrum sensing in cognitive radio

Cognitive radio (CR) has emerged as a promising solution to the problem of spectrum underutilization. In CR, spectrum sensing is a key feature. It enables the cognitive user or secondary user (SU) to detect spectrum holes and ensure non-interference to primary communication. Spectrum sensing has its own challenges, such as discovery of opportunities for transmission and sensing overhead. High sensing overhead may impair spectral efficiency as the radio is mostly used for detecting primary users (PUs), rather than transmitting data. On the other hand, a less frequent sensing may result in interference to PU, due to the delay in the detection of the PÚs reappearance and can lead to loss of transmission opportunities. Thus, it is of paramount importance to optimize the sensing periods for each primary channel in order to maximize the number of transmission opportunities and reduce the sensing overhead incurred. This paper extends our previous letter (Balieiro, Yoshioka, Dias, Cavalcanti, & Cordeiro, 2013) and presents a detailed description of our adaptive sensing optimization scheme for CR Networks based on a multi-objective genetic algorithm (GA) formulation. Our scheme aims at maximizing the spectrum opportunities as well as keeping the sensing overhead always within a user-defined maximum value. The simulation results show that the proposed scheme outperforms the schemes described in the literature, while keeping the sensing overhead within a target value. In addition, it provides different levels of protection to PU communication through the configuration of threshold for sensing overhead.

Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants

Surveillance tests are performed periodically on standby systems of a Nuclear Power Plant (NPP), as they improve the systems' availability on demand. High availability of safety critical systems is very essential to NPP safety, hence, careful analysis is required to schedule the surveillance activities for such systems in a cost effective way without compromising the plant safety. This forms an optimization problem wherein, two different cases can be formulated for deciding the value of Surveillance Test Interval. In one case, cost is the objective function to be minimized while unavailability is constrained to be at a given level and in another case, unavailability is minimized for a given cost level. Here, optimization is done using Genetic Algorithm (GA) and real encoding has been employed as it caters well to the requirements of this problem. A detailed procedure for GA formulation is described in this paper. Two different crossover methods, arithmetical crossover and blend crossover are explored and compared in this study to arrive at the most suitable crossover method for such type of problems.

Adaptive spectrum sensing for cognitive radio based on multi‐objective genetic optimisation

An adaptive sensing period optimisation scheme for cognitive radio networks based on a multi-objective genetic algorithm formulation is proposed. The proposed scheme aims at maximising the spectrum opportunities as well as minimising the incurred sensing overhead. The simulation results show that the proposed scheme outperforms the non-optimised proposals by up to 90%. It can also obtain similar or superior performance to the compared schemes described in the literature, while keeping the sensing overhead within a target value.

Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis

This work proposes an analytical method for cereal bar classification based on the use of near infrared spectroscopy (NIRS) and supervised pattern recognition techniques. Linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of variables (wavenumbers). For the purpose of variable selection, three techniques are considered, namely successive projection algorithm (SPA), Genetic Algorithm (GA), and stepwise (SW) formulation. The methodology is validated in a case study involving the classification of 121 cereal bar samples into three different types (conventional, diet and light). The results show that the LDA/GA model is superior to the LDA/SPA and LDA/SW models with respect to classification accuracy in an independent prediction set. Some advantages of the proposed method are speed, that the analytical measurement is performed quickly (one minute or less per sample), no reagents, low sample consumption and minimum sample preparation demands. In view of the results obtained in this study the proposed method may be considered valid for use in cereal bar classification.

HGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules.

A computationally efficient reduced order model to generate multi-parameter fluid-thermal databases

A reduced order model (ROM) is proposed to generate multi-parameter databases of some fluid-thermal problems, using a combination of proper orthogonal decomposition, a gradient-like method, and a continuation method. The resulting ROM greatly reduces the CPU time required by slower methods based on genetic algorithm formulations. As a byproduct, the number of required snapshots is also reduced, which yields an additional improvement of the computational efficiency. The work presented in this article aims to facilitate the use of ROMs in industrial environments, in which time is a very important asset. The methodology is illustrated with the non-isothermal flow past a backward-facing step in the laminar regime, which is a representative problem, related to the engineering design of micro-heat sinks.

Penggunaan Kaedah Algoritma Genetik dalam Permasalahan Penjadualan PCB: Kajian Kes di Industri Elektronik

Production planning and scheduling functions play important roles in production especially to ensure the availability of manufacturing resources needed to accomplish production tasks. This paper proposes the use of Genetic Algorithm in solving scheduling problems and the case study has been conducted in printed circuit board assembly company. Genetic algorithm formulation included chromosome representations and genetic operators have been developed to suit the scheduling problems that take into account the alternative production lines. Simulation results have been compared to the manual schedules. It shown that the genetic algorithm is capable to produce schedules with shorter assembly time. It is can accommodate additional elements such as maintainance time quite easily. This is done by adding the maintenance time slot within the chromosome. Another experiment has been done by using dispatching rule in sequencing the models on the assembly lines. Genetic Algorithms have shown a promising results compared to manuan and dispatching rule.