Genetic Algorithm Procedure Research Articles

Fitting x-ray diffraction peaks by a genetic algorithm and Monte Carlo Markov Chain procedure

A fitting procedure based on the use of the Genetic algorithms (GA) and the Markov Chain Monte Carlo (MCMC) bayesian technique is proposed for the modelization of the x-ray diffraction peaks by using the usual standard bell-type functions (pseudo-Voigt, Voigt, etc). The method has been tested with simulated Voigt-shaped profiles at different overlapping degrees and the results have been compared with those obtained by means of the classical Levenberg-Marquardt algorithm, showing its reliability.

Optical Properties of Three‐Electron GaAs/AlxGa1−xAs QDs with Finite Confinement Potential

AbstractIn the case of finite confinement potential, the average energies and corresponding wave functions for the 1s2nl configurations, in which nl = 2s, 2p, 3d, and 4f, of three‐electron GaAs/AlxGa1−xAs quantum dot with and without impurity are computed by using a new variational approach which is a combination of Quantum Genetic Algorithm procedure and Hartree–Fock–Roothaan method. Using the calculated average energies and wave functions, a detailed investigation of the linear, third‐order nonlinear and total absorption coefficients (ACs) and the refractive index changes (RICs) for the quantum dot is performed, and the obtained results are presented as a function of dot radius and photon energies. The results show that the dot radius, the impurity charge, and the height of potential barrier have a strong influence on the average energies and absorption spectra of the system. As the potential barrier height increases, the peak positions of the ACs and RICs shift toward higher energy, and there is a significant increase in the amplitudes of the absorption spectra as the potential barrier height increases. In addition, the electron wave functions begin to enter the quantum well at smaller dot radii with increasing barrier height.

Combining balancing, sequencing and buffer allocation decisions to improve the efficiency of mixed-model asynchronous assembly lines

When the production of a variety of models is considered in assembly lines, the more suitable control system is asynchronous: the pace of the line is not imposed but depends on the actual time needed in each work centre to complete a product. In asynchronous lines blocking and starvation phenomena are responsible of performances deterioration. Three popular techniques able to limit these phenomena are: balancing the workload among stations by tasks assignment, allocating buffers between stations, optimizing of the sequence of models entering the line. These three techniques correspond to the Mixed-model Assembly Line Balancing Problem (MALBP), the buffer allocation problem (BAP) and the sequencing problem (SP). The presented approach is able to simultaneously solve these three problems, which are strictly connected, and is based on a Genetic Algorithm procedure that uses discrete event simulation to evaluate the fitness function of individuals. Tests conducted on a set of benchmark instances allow quantifying the effect on line efficiency of sequencing and buffer allocation decisions separately, with the aim to assess which of the two techniques is more effective and to investigate whether if combining sequencing optimization and buffer allocation gives additional advantages in terms of increasing line efficiency.

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.

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

A nondominated sorting genetic model for co-clustering

Co-clustering aims to cluster the rows and columns of data simultaneously and can be often formulated as a two-objective optimization problem (one objective for rows and the other for columns) and the solution is a Pareto-optimal solution set in principle. Existing methods usually convert the co-clustering problem into a single-objective optimization problem by setting a hyper-parameter λ between the two objectives. However, finding a good value of λ is not easy because of its large parameter space; also, there may exist many equally good λs. In this paper, a nondominated sorting genetic model (NSGC) is proposed to tackle the co-clustering problem, totally bypassing the trade-off parameter λ and returning to the original two-objective problem. The core of our model is to group a row objective function and a column objective function and integrate them into a genetic algorithm as the fitness functions. After this reformulation, we follow a standard genetic algorithm procedure to iteratively find the Pareto-optimal solutions. Finally, to fish out a single best solution we further design a sorting criterion according to which the Pareto-optimal solution set can be totally ordered. Extensive experiments with 16 public datasets are conducted, and the results demonstrate the superiority of our approach.

Energy-saving design and optimization of pressure-swing-assisted ternary heterogenous azeotropic distillations

A huge amount of energy is always consumed to separate the ternary azeotropic mixtures by distillations. The heterogeneous azeotropic distillation and the pressure-swing distillation are two kinds of effective technologies to separate heterogeneous azeotropes without entrainer addition. To give better play to the synergistic energy-saving effect of these two processes, a novel pressure-swing assisted ternary heterogeneous azeotropic distillation process is proposed firstly. In this process, the ternary heterogeneous azeotrope is decanted into two liquid phases before refluxed into the azeotropic distillation column to avoid the aqueous phase remixing, and three columns’ pressures are modified to decrease the flowrates of the recycle streams. Then the dividing wall column and heat integration technologies are introduced to further reduce its energy consumption, and the pressure-swing assisted ternary heterogeneous azeotropic dividing wall column and its heat integration structure are achieved. Genetic Algorithm procedure is employed to optimize the proposed processes. The design results show that the proposed processes have higher energy efficiencies and lower CO2 emissions, compared with the published ternary heterogeneous azeotropic distillation process.

Algorithm for allocating virtual computers in energy-friendly cloud data centers

A huge proportion of centres that consume a lot of power, skyrocketing operating costs, and emit a disproportionate amount of carbon dioxide appeared due to the constantly increasing need for processing capacity. This necessitates finding a better approach to handling the energy crisis. This paper proposes a solution to address the energy crisis in cloud data centres, which consume a significant amount of power and emit a large amount of carbon dioxide. A power-conscious simulated engine allocation to physical machines reduces power consumption while maintaining an effective Class of Use. The proposed method is compared with other existing methods such as the Genetic Algorithm (GA), Modified Best Fit Decreasing Algorithm (MBFD), Random Selection (RS), and Minimum Migration Time (MMT) in terms of power usage. The proposed method outperforms the MBFD and GA procedures, showing an 8.55% and 46.81% improvement in power usage, respectively. The MBFD and GA procedures consume 389.5 and 812 kilowatts of energy, while the proposed method reduces this energy consumption. The proposed method is validated using the CloudSim simulator, which demonstrates its success as the fastest and most intelligent procedure compared to other methods. Additionally, the proposed method addresses the burdened host challenge quickly and provides valuable insights for future research by utilizing deep learning approaches.

Genetic algorithm for obtaining potential energy curve of diatomic molecules based on dispersed fluorescence spectra

The method for reconstruction of an adiabatic potential energy curve from experimental dispersed fluorescence spectra has been developed. The novelty of the method relies on a unique approach of simultaneous use of bound rightarrow bound and bound rightarrow free parts of the spectrum. The method is based on the Genetic Algorithm (GA) procedure and determines potential energy curve integrally, below and above the dissociation energy limit. The method was tested on the artificially generated reference spectrum as well as experimental spectrum of G0_u^+(upsilon ^{prime }=39) rightarrow X0_g^+ transition in Hg_2. The tests show very good accuracy of simulation based on GA results with artificially generated reference spectrum as well as with the experimental one.

Serum infrared spectral profile is predictive of the degree of hepatic fibrosis in chronic hepatitis C patients

Assessment of liver fibrosis is crucial to guide the therapeutic strategy in patients with chronic liver disease. We investigated the potential of serum Fourier transform infrared (FTIR) spectroscopy for assessing the degree of hepatic fibrosis in patients with chronic hepatitis C (CHC). The study was conducted on dried serum samples from 94 CHC patients at different histological stages of hepatic fibrosis: METAVIR F0 (n = 20), F1 (n = 17), F2 (n = 20), F3 (n = 20) and F4 (n = 17). Transmission FTIR spectra were acquired in the 4000–400 cm−1 range. Wavenumbers were selected by genetic algorithm (GA) according to their diagnostic performance as assessed by a partial least squares discriminant analysis (PLS-DA) model using a training and a validation set to differentiate severe stages of fibrosis from mild or moderate ones. The GA procedure was applied 50 times on randomly selected sets. Furthermore, the best set of wavenumbers was re-tested in 1000 randomly selected validation sets. Wavenumbers selected by GA corresponded to functional groups present in lipids, proteins, and carbohydrates. This model allowed to identify patients with cirrhosis (METAVIR F4), patients with advanced fibrosis (METAVIR F3 and F4), and patients with significant fibrosis (METAVIR F2, F3 and F4), with AUROC (Area Under the Receiver Operating Characteristic) of 0.88, 0.85 and 0.85, respectively. Thus, serum FTIR spectroscopy appears to have a strong potential as a new diagnostic tool for assessing the degree of fibrosis in patients with chronic liver disease.

Techno-economic analysis on nitrogen reverse Brayton cycles for efficient coalbed methane liquefaction process

Recovery of coalbed methane (CBM) is of great significance for environmental protection, safe production of coal mines, and clean energy supply. To reveal the efficiency difference of different nitrogen reverse Brayton cycles (N2-RBC) for recovering CBM and identify the most efficient N2-RBC in terms of energy consumption and economy, four typical N2-RBCs were studied numerically. Each process was optimized globally by performing genetic algorithm (GA) procedure, using specific power consumption (SPC) per unit product as the objective function. Pinch analysis approach was used to improve N2-RBC. All proposed processes were assessed comprehensively concerning efficiency, exergy loss, and economy. It is found that the slope of the hot/cold composite curve is greatly influenced by the material streams’ mass flow rate while slightly affected by the specific heat except for the region near critical parameters. The parallel arrangement of expanders reaches a better match between hot and cold composite curves than the series. For the liquefaction capacity of 10 tons per day, the liquefaction process using parallel N2-RBC achieves the lowest SPC of 0.72 kWh·kg−1 and the highest figure of merit of 31.11%. It also has the lowest exergy loss of 180.1 kW and a total investment of 4047.76 k$, respectively.

Energies of the ground and excited states of confined two-electron atom in finite potential well

For the ground and excited configurations, we have carried out the energy eigenvalues and corresponding wavefunctions of two-electron atom enclosed by finite confining potential by using Quantum Genetic Algorithm (QGA) procedure and Hartree-Fock Roothaan (HFR) method. Besides, we have analysed in detail orbital energies and probabilities of finding electron for the configurations 1S (1s2), 3S (1s2s), 3P (1s2p), 3D (1s3d) and 3F (1s4f). The results reveal that the potential barrier height plays a significant role on average energies, state energies, orbital energies and probabilities of finding electrons in orbitals. As dot size is extremely small and extremely large, the energies for 1snl configurations are getting close to each other, but between these two extreme situations they are separating from each other. While going to higher excited states, singlet and triplet states are almost the same due to the weakening of the exchange energy.

Program untuk permasalahan multiple trip vehicle routing problem (MTVRP) menggunakan algoritma memetic pada proses pendistribusian

In everyday life almost all problems require the help of mathematics, one of them on the transportation or on the distribution prosess. Multiple Trip Vehicle Routing Problem (MTVRP) is one of the problems related to the transportation or distribution prosess. Multiple Trip Vehicle Routing Problem (MTVRP) is defined as the problem of the Vehicle Routing Problem (VRP) with the expansion and the addition of multiple trips on the each vehicle when it distributes goods and the time window of customer service. One algorithm to solve the Multiple Trip Vehicle Routing Problem (MTVRP) is memetic algorithms. Memetic algorithm is a combination of genetic algorithm and local search procedures that intensify the search. Memetic algorithms procedures are: initialization process, evaluation process, selection, crossover process, mutation process, repair process, local search, vehicle allocation, and the best route is formed. To facilitate the search for the solution of the Multiple Trip Vehicle Routing Problem (MTVRP) especially at the time had to submit to many customers, memetic algorithms implemented in the language programming Borland Delphi. By using an program application be made, produced together with the results obtained manually route 0 – 1 – 3 – 4 – 0 – 5 – 2 – 0 with a travel time "1.1675" hours and uses a vehicle. This program is designed applications up to 50 points in the Multiple Trip Vehicle Routing Problem (MTVRP) using memetic algorithms, which is already in trials with 11 points, 22 points, and 50 points in attachment. So that the application program can be used to solve the Multiple Trip Vehicle Routing Problem (MTVRP) using memetic algorithms on the transportation or on the distribution prosess.

Minimizing Makespan in a Permutation Flow Shop Environment: Comparison of Scatter Search, Genetic Algorithm and Greedy Randomized Adaptive Search Procedures

Solving scheduling problems enables more efficient use of production capacity. It involves defining the sequence of operations, determining the capacity of resources, and balancing workloads. Different methods, especially metaheuristics, have been used to solve these problems. This study presents the application of Scatter Search (SS), Genetic Algorithm (GA), and Greedy Randomized Adaptive Search Procedures (GRASP) for minimizing makespan in a permutation flow shop environment. In this study, the performances of these methods are compared through various test problems in the literature and a real-life problem of a company operating in the automotive sector. Study comprises 48 jobs that must be planned within a day for eight consecutive operations. In cellular manufacturing, the sequence in which each job is processed in eight operations is the same. In solving permutation flow shop scheduling problems (PFSP), one of the NP-hard problems, meta-heuristic methods are widely applied due to their successful results. From this point of view, SS, GA, and GRASP are employed in this study, and their performances are compared.

New single-column extractive distillation with heavy entrainer to separate binary azeotropic mixtures

Extractive distillation with heavy entrainer is widely used in the separation of binary azeotropic mixtures. While a large amount of energy is always consumed in conventional two-column processes. A new flash tank-assisted single-column extractive distillation with heavy entrainer (FT-SCED) is proposed in this paper. The side vapor-phase stream with high heavy component content is withdrawn from the extractive column, and then it is partly condensed in a flash tank. By this means, the high-purity light and heavy products can be achieved from the overheads of column and flash tank, respectively. To obtain the proposed process, an initial single column extractive distillation with heavy entrainer is designed firstly. Then the flash tank is added into the process on the basis of the thermodynamic feasibility analysis. Genetic Algorithm procedure is employed to optimize the independent variables of the obtained process. Two case studies show that the proposed process has lower annual total cost compared with conventional extractive distillation and extractive dividing wall column. PI control strategies are established for the proposed processes in two cases. The dynamic control results show that these control strategies handle feed flowrate and composition disturbances excellently.

Surrogate Based Genetic Algorithm Method for Efficient Identification of Low-Energy Peptide Structures.

Identification of the most stable structure(s) of a system is a prerequisite for the calculation of any of its properties from first-principles. However, even for relatively small molecules, exhaustive explorations of the potential energy surface (PES) are severely hampered by the dimensionality bottleneck. In this work, we address the challenging task of efficiently sampling realistic low-lying peptide coordinates by resorting to a surrogate based genetic algorithm (GA)/density functional theory (DFT) approach (sGADFT) in which promising candidates provided by the GA are ultimately optimized with DFT. We provide a benchmark of several computational methods (GAFF, AMOEBApro13, PM6, PM7, DFTB3-D3(BJ)) as possible prescanning surrogates and apply sGADFT to two test case systems that are (i) two isomer families of the protonated Gly-Pro-Gly-Gly tetrapeptide (Masson, A.; J. Am. Soc. Mass Spectrom.2015, 26, 1444-1454) and (ii) the doubly protonated cyclic decapeptide gramicidin S (Nagornova, N. S.; J. Am. Chem. Soc.2010, 132, 4040-4041). We show that our GA procedure can correctly identify low-energy minima in as little as a few hours. Subsequent refinement of surrogate low-energy structures within a given energy threshold (≤10 kcal/mol (i), ≤5 kcal/mol (ii)) via DFT relaxation invariably led to the identification of the most stable structures as determined from high-resolution infrared (IR) spectroscopy at low temperature. The sGADFT method therefore constitutes a highly efficient route for the screening of realistic low-lying peptide structures in the gas phase as needed for instance for the interpretation and assignment of experimental IR spectra.

Seismic upgrading of RC structures through an optimization procedure based on Genetic Algorithm

Nowadays governments are encouraging the upgrading and the reuse (rather than the demolition) of older structures in order to reduce land use and environmental impact due to the construction of new buildings. The choice of the most suitable intervention for the seismic upgrading of existing structures could also be addressed combining member-level (e.g., FRP-confinement of single columns) and structural-level (e.g., insertion of steel bracing systems) techniques, although it may result in a complex technical challenge for engineers, since a huge number of combinations of technically feasible upgrading interventions are theoretically possible to accomplish the desired structural performance. In order to support the intervention choice, an “objective” approach could be implemented making use of recently-invented Artificial Intelligence (AI) procedures. Specifically, the application of Genetic Algorithms (GAs) is usually thought as a suitable optimization procedure in several civil engineering problems. By means of a Genetic Algorithm (GA), the design of upgrading interventions results to be based on one objective criterion related to the cost-effectiveness of the intervention, rather than on the highly subjective “engineering-judgement”. The present paper aims to highlight the capability of a Soft-Computing (SC) procedure in selecting the most cost-effective combination of the aforementioned member-level and structural-level interventions, among the technically consistent ones. To this aim a parametric study on a RC structure based on a similar GA procedure is reported, varying some “engineering parameters” related to the target seismic risk class of the upgraded structure. Relevant differences in the results are observed due to the variation of target of the analyses.

Genetic Algorithm For Multi-Objective Optimization in Sculptured Dies-Cavity Roughing

Sculptured-dies Cavity Roughing (SDCR) problem is a multi-dimensional problem. In XY-single cutter problem, the decision variables consist of layering and tooling selection problem by maximizing the efficiency of roughing, which consider finishing efficiency. The previous research approach to this problem shows that the dynamic programming approach. However, it is effective in searching solutions for the time-to-volume coefficient minimization (TVC) problem, and empirically shows 10% improvement compared to machining time minimization objective. The pre-processing procedures in dynamic programming approach are quite complex time-consuming. Applying a genetic algorithm procedure for the multi-dimensional problem (GAMD) guarantees the merging process's feasibility, these pre-processing procedures can be eliminated, and significantly faster computational time. In the 7-3-3 problem chosen in this research, the computational time is reduced from about 2 hours to 30 seconds.

Evaluation of Back of Queue at Signalized Intersections in Erbil City Using Different Software

Aims: The aim of this study is to evaluate the queue length of isolated signalized intersections, which used HCS2010 and SIDRA Intersection 8 traffic software. Background: One of the important parameters to evaluate the performance of a traffic management system is queue length, especially in congested traffic situations. For the estimation of back of queue length, many traffic analyzing models are used based on Highway Capacity Manual (HCM2010). Objective: The values of queue length obtained from these two softwares are to be compared with observed queue length from the field. The study also aids in conducting a model of optimization for the back of queue length to decrease the value of storage length of vehicles following the GA procedure. Methods: In this study, four isolated signalized intersections were used in Erbil city, the capital of the Iraqi Kurdistan Region. The values of the measured back of queue lengths are compared with the values obtained from Highway Capacity Software HCS2010(HCS+T7F) and Signalized and Unsignalized Intersection Design and Research (SIDRA Intersection 8) Aid Australian Road Research Board (ARRB) software. Results and Discussion: The results of regression analysis showed that the SIDRA Intersection 8 Back of Queue model with adjusted R2 0.8465 have a stronger relationship with the field measured Back of the Queue for linear relationship compared to the HCS2010 back of queue model. Furthermore, the study tends to optimize the signalized intersections under study using HCS2010 and SIDRA Intersection 8 software based on the Genetic Algorithm (GA) to minimize the back of queue length. The percent of average reduction in HCS2010 and SIDRA Intersection 8 back of queue models are 36% and 29%, respectively. The main objective of optimizing the timing plan was to minimize the average storage queue length at signalized intersections that follow the Highway Capacity Manual (TRB 2010) procedure for queue length calculation. Moreover, 95th and 98th values of the percentiles were chosen to estimate the expectancy of upstream lane blockage and the expectancy of short lane overflow that was related to the back of the queue percentile. Conclusion: The improvement of vehicle storage for upstream lanes was conducted by the two software systems mentioned before. The results showed significant improvement in the performance of the intersections under study with a reduction in queue length.

Optimization Design of Automotive Body Stiffness Using a Boundary Hybrid Genetic Algorithm

At the conceptual design stage, it is critical to use appropriate structural analysis and optimization methods. The thin-walled beam transfer matrix method (TBTMM) is adopted to establish the mathematical model of the simplified vehicle body-in-white (BIW) structure in this paper and compare it with the results of the finite element method (S-FEM) to verify the approach. In addition, on the basis of the boundary simulation genetic algorithm (BSGA) and local search procedure, a boundary hybrid genetic algorithm (BHGA) is proposed. BHGA is benchmarked on 20 test functions and is compared with current meta-heuristic algorithms to prove its effectiveness and universality. Finally, considering the bending and torsional stiffness constraints, BIW conceptual model is lightweight and designed with an optimizer.