Genetic Algorithm Code Research Articles

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Development of a Genetic Algorithm Code for the Design of Cylindrical Buoyancy Bodies for Floating Offshore Wind Turbine Substructures

The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for floating substructures of offshore wind turbines. The presented work is based on a previously studied buoyancy body design that allows an efficient manufacturing process and integration into different substructures. In this study, an optimization framework based on genetic algorithm is developed to parameterize the buoyancy body’s geometry and optimize its design in terms of cost, considering loads acting on the structure as well as manufacturing and floater specific dimension restrictions. The implementation of the optimization process is detailed, and tested for a given study case. Two structurally different genetic algorithms are considered in order to compare the results obtained and asses the performance of the presented optimization framework.

Optimization of Apron Support Vehicle Operation Scheduling Based on Multi-Layer Coding Genetic Algorithm

Optimization of Apron Support Vehicle Operation Scheduling Based on Multi-Layer Coding Genetic Algorithm

Optimization of Thermal Management in Modern Electric Vehicle Battery Cells Employing Genetic Algorithm

Abstract Optimization of thermal performance processes using genetic algorithm (GA) combined with some commercial software or other soft computing methods like artificial neural networks are common in many heat transfer applications with the exception of battery thermal management. In this article, a novel and innovative approach for single-objective optimization using GA combined with in-house developed finite volume method (FVM)-based code is investigated. Three important thermal and fluid flow performance parameters of modern electric vehicle Lithium–ion battery cells, namely, average Nusselt number (Nuavg), friction coefficient (Cf,avg), and maximum temperature (T¯max) are optimized. The operating parameters considered for optimization include heat generation term (S¯q), Reynolds number (Re), conduction-convection parameter (ζcc), aspect ratio (Ar), and spacing between the cells (W¯ff) varying in some selected range. Optimization in case of internal flow between the battery cells and external flow over the battery cell is performed. Computational time taken by the combined GA and FVM code for 5, 10, 15, and 20 iterations in case of internal and external flow is also presented. From the complete optimization analysis, it is found that for higher charging/discharging rates at which the heat generation is very high, T¯max can be kept within the safe limit, Nuavg to maximum and Cf,avg to a minimum with a slight compromise in pumping power requirement to circulate the coolant in internal flow. For external flow analysis, Re and ζcc in a selected medium range will provide optimized thermal and fluid flow situations.

Single node failure repair method for distributed storage system based on genetic algorithm and MSR code

In view of the minimum storage regeneration (MSR) single fault node repair plan, design a kind of new single repair fault node topology. Consider link bandwidth available and the node processing power of heterogeneity, the repair delay and repair traffic node of combinatorial optimization. Topology can be converted to Steiner tree model with constraints, and design the corresponding hybrid genetic algorithm to solve the approximate global optimal solution. Finally, a compromise between the two optimization objectives is achieved. Simulation results show that under the same MSR code size, the repair delay of this topology is only 60%-80% of that of the traditional tree topology and 30%-40% of that of the traditional star topology. While the repair traffic for this topology is 9% to 65% higher than for the traditional star topology, the repair traffic is only 40% to 55% higher than for the traditional tree topology.

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.

Active flow control optimisation on SD7003 airfoil at pre and post-stall angles of attack using synthetic jets

The use of Active Flow Control (AFC) technologies to modify the forces acting on streamlined bodies is one of the most active research fields in aerodynamics. For each particular application, finding the optimum set of AFC parameters which maximises lift, minimises drag or maximises lift-to-drag ratio (aerodynamic efficiency), has become a necessary design requirement. In the present paper, the AFC technology was applied to the Selig-Donovan 7003 (SD7003) airfoil at Reynolds number 6×104. Synthetic jets were employed to modify the lift and drag forces acting on the airfoil. Four angles of attack (AoA) of 4∘, 6∘, 8∘ and 14∘ were considered, alongside five AFC parameters: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. A multi objective optimisation based on genetic algorithms (GA) was performed for each angle of attack to find the optimum combination of AFC parameters. Each GA generation was simulated using Computational Fluid Dynamics (CFD). A home-made GA package was linked with a mesh generator and the CFD solver, and the results were automatically fed back to the GA code. Over 2200 CFD simulations were performed in two dimensions, using the Spalart-Allmaras turbulent model. The motivation behind the current study is to understand the dependence of the optimum set of AFC parameters on the AoA. Results show that, as AoA is increased, the potential benefits of AFC become more pronounced, which allows for considerable improvement in aerodynamic efficiency. The physics involved in the interaction between the main flow and synthetic jet are clearly presented and clarifies that the physical phenomenon to obtain maximum efficiency is completely different at pre-stall and post-stall AoA. In particular, the aerodynamic efficiency was increased by 251% from baseline (no actuation) by using a moderate/finite momentum coefficient at AoA=14∘, while a mere 39% increase was obtained at AoA=8∘. In addition, the interaction between the incoming flow and the synthetic jet pulsating flow at different injection angles has been thoroughly investigated.

Analysis of HL-2A charge exchange spectra using parallel genetic algorithm

In this work, we present a new method based on parallel genetic algorithm (GA) for in-between shot data analysis of the Charge-Exchange (CX) spectra on the HL-2A tokamak. The neutral beam induced active CX spectra is a powerful ion diagnostic technique to provide spatially resolved ion temperature and rotation velocity measurements on fusion devices. Currently CX spectra obtained in HL-2A experiments are mainly analyzed by the CXSFIT code [A. D. Whiteford, et.al, 2007]. While the analysis itself is fast, its accuracy relies on proper setup of the initial values for the spectral fitting parameters. Time-consuming manual interventions are needed. In the new parallel GA code, a two-loop GA analysis is used to gradually update the fitting parameter search ranges, which enables automatic analysis. A parallel algorithm based on the Linux Message Passing Interface (MPI) cluster is adapted to speed up the process. In a test run, for a set of 1600 data slices, the total time elapsed with 8 CPU nodes is about 310 s (0.2 s per data slice), which is efficient for in-between shot analysis on HL-2A. The uncertainty calculations using virtual CX signals with a noise level up to 5% show that the accuracies for ion temperature and rotation velocity are better than 10.14% and 2.14%, respectively. The ion temperature and rotation velocity obtained by applying the new parallel genetic algorithm on experimental CX data show good agreement with the conventional CXSFIT results.

Solar Hydrogen System Configuration Using Genetic Algorithms

For standalone power supply systems based on solar hydrogen technology to work efficiently, the photovoltaic generator and electrolyser stack have to be con?gured so that they produce the needed amount of hydrogen in order for the fuel cell to produce sufficient power to operate the load. This paper discusses how genetic algorithms were applied to optimise the design of the photovoltaic generator and electrolyser combination by searching for the best con?guration in terms of number parallel and series PV modules, number of electrolyser cells, and cell surface area. First, a mathematical simulation model based on the current-voltage PV characteristics and the polarisation characteristics of the electrolyser was developed. The models parameters were obtained by ?tting the mathematical models to experimental data. A genetic algorithm code was then developed. The code is based on the PV and electrolyser models as an evaluation measure for the ?tness of the solutions generated. Results are presented con?rming the effectiveness of using the genetic algorithm technique for solar hydrogen system con?guration.

Superatomic Signature and Reactivity of Silver Clusters with Oxygen: Double Magic Ag 17 – with Geometric and Electronic Shell Closure

Understanding the stability and reactivity of silver clusters toward oxygen provides insights to design new materials of coinage metals with atomic precision. Herein, we report a systematic study o...

A blackbox optimization of volumetric heating rate for reducing the wetness of the steam flow through turbine blades

This paper proposes to use a blackbox optimization to obtain the optimal volumetric heating required to reduce the wetness at the last stages of steam turbines. For this purpose, a global multiobjective optimization is utilized through the automatic linking of genetic algorithm and CFD code, where the blackbox function evaluations are performed by CFD runs. The logarithm of number of droplets per volume (LND), the droplet average radius (DAR), and the integral of local entropy (ILE) at the end of the cascade (after the condensation location) are minimized, while the volumetric heating rate is the optimization parameter. The Eulerian–Eulerian approach is implemented to model the two-phase wet steam turbulent flow and the numerical results are validated against well-established experiments. Since higher volumetric heating rates reduce DAR and LND, while increase ILE, according to optimization results, there is an optimum for the volumetric heating rate to reach the best performance of steam turbines. For case studies presented in this work, the optimal volumetric heating rates of 5.21×108 and 4.67×108 W/m2 are obtained for two different cases of supersonic and subsonic outlets, respectively. Particularly, these rates improve DAR by 45.7% and 57.5%, and LND by 6.0% and 7.8% for respective cases.

Comparative double and integer optimization of low-grade heat recovery from PEM fuel cells employing an organic Rankine cycle with zeotropic mixtures

In the present study, a comparative optimization analysis of a hydrogen-based proton exchange membrane (PEM) fuel cell integrated with an organic Rankine cycle (ORC) using twenty different zeotropic mixtures is accomplished. Accordingly, considering the mixture type as a qualitative decision variable, a novel method of integer single/multi-objective optimization is implemented from thermodynamic and economic aspects. Using a developed genetic algorithm code in MATLAB software, histogram and scatter distributions are presented to determine the density of optimum points and optimum fraction for each mixture. The optimal solution points of exergy efficiency and total cost rate for each mixture are extracted via a Pareto frontier diagram. Eventually, to assess the influence of major decision variables on system performance, a comparative parametric study on five optimal mixtures is carried out. Referring to single-objective optimization results of the ORC unit and the overall system, the use of R601/Cis-2-Butene (2/98) and R601a/Cis-2-Butene (1.32/98.68), respectively, lead to the highest exergy efficiency. Also, considering exergy efficiency as objective, the results of optimization indicates that at optimal condition, the temperature difference between the PEM fuel and evaporator temperature is 13 K. Results further indicate that while a high-temperature PEM fuel cell is a suitable option from an exergy maximization aspect, a low-temperature PEM fuel cell is superior from multi-objective optimization viewpoint. Results of multi-objective optimization reveal that R601a/Hexane (13.32/86.68) and R601a/C-2-Butene (20.14/79.86) are the best mixtures. Furthermore, what stands out from scatter distribution is that most of the optimal points of evaporator temperature are between 305 K and 380 K. Comparative parametric study results demonstrate that in the selected range of major decision variables, R601a/Cis-2-Butene (20.14/79.86) and R601a/Hexane (13.32/86.68) are the best optimum mixtures from an economic facet.

Effect of fiber exclusion in uniaxial tensile tests of soft biological tissues

The effect of the exclusion of the compressed fibers in the identification of material parameters from uniaxial tensile tests on two orthogonal strips is investigated. The micro-structurally based constitutive model with two dispersion parameters developed by Holzapfel and his colleagues is utilized in the study. A new exclusion method, based on the coefficient reflecting the percentage of stretched fibers, is proposed. The material parameters are identified by using experimental data from 30 uniaxial tensile tests (5 donors, 6 strips per donor) and a genetic algorithm code that is capable to find the optimal set of parameters. The contraction of the strip width computed by using the hyperelastic model with the identified material parameters is compared to the experimental data for two human aortas (one from literature and one experiment, specific for this study), in order to show the accuracy of the identified model. The complex behavior of the thickness deformation of the strip is also obtained and compared to the experimental data derived from in-plane measurements and the incompressibility condition. Results show that the in-plane fiber exclusion is appropriate for aortic material characterization with uniaxial tensile tests, reducing very significantly the computational cost. At the same time, thickness growth of strips during uniaxial tests is possible, depending on fiber dispersion and orientation.

Atomistic study on the pressure dependence of the melting point of NdFe12

We investigated, using molecular dynamics, how pressure affects the melting point of the recently theorised and epitaxially grown structure NdFe12. We modified Morse potentials using experimental constants and a genetic algorithm code, before running two-phase solid-liquid coexistence simulations of NdFe12 at various temperatures and pressures. The refitting of the Morse potentials allowed us to significantly improve the accuracy in predicting the melting temperature of the constituent elements.

Coordinated Anti-Collision Path Planning Algorithm for Marine Surface Vessels

This article presents a coordinated anti-collision path planning algorithm for marine surface vessels in complex multi-ship encounter. Unlike previous research that follows the optimization sequence one step after the other from the prospective of single ship, a multi-stage anti-collision strategy from the overall perspective is established based on the principles of Synergetic theory and Queueing theory, which guarantees the compliance with navigation practice and the coordination among ships' avoidance actions. The concept of an optimization echelon is proposed to help categorize the optimization priority. Then a Multilayer Coding Genetic Algorithm enables all ships belonging to the same optimization echelon participate in the decision-making process simultaneously. Furthermore, the quantitative model of encounter situation is refined through the sensibility analysis. Finally, simulation examples consisting different traffic scenarios are analysed to demonstrate the effectiveness and practicality of the proposed scheme.

Genetic Algorithm Optimisation of a TNT Solidification Model

The control of the solidification process of energetic materials is important to prevent manufacturing defects in high explosive ammunitions. The present work aims to propose an optimisation procedure to determine the value of the model parameter, avoiding the traditional trial and error approach. In this work, the solidification of TNT has been numerically modelled employing apparent heat capacity method and the model parameter was optimised using genetic algorithm. One dimensional numerical model has been solved in Comsol Multiphysics Modeling Software and the genetic algorithm code was written in Matlab. The Neumann’s analytical solution of the solidification front was used as a reference to build the fitness function, following the inverse problems concepts. The optimum model parameter has been predicted after 20 generations and among 30 candidate solutions for each generation. The numerical solution performed with the optimised model parameter has agreed with the analytical solution, indicating the feasibility of the proposed procedure. The discrepancy was 3.8 per cent when maximum difference between analytical and numerical solutions was observed.

Structural, electronic and magnetic properties of bimetallic PdCo nanoparticles with/without metal oxide support and their interactions with nitric oxide (NO): a first principle (ab initio) material modelling study

We have applied ab initio electronic structure method to search minimum energetic structures of the supported and free bimetallic PdCo nanosized particles by using unbiased (surface modified) Birmingham Cluster Genetic Algorithm code, coupling with density functional theory including dispersion correction (Van der Waals). A detailed analysis of structural motifs and segregation effects of free and supported PdCo nanoparticles has been performed by investigating more than one thousand isomers. Alloying and oxide support effects on bimetallic PdCo in term of electronic, magnetic, structural, energetic and stability were also examined comprehensively by calculating pdos, lowden charge analyses, charge density (differences), second finite energies, mixing (formation) energies, dipole moments, ionization energies, electron affinities, chemical reactivity descriptors and HOMO-LUMO gaps. The optimized structures were characterized by vibrational, dipole moment, STM and XRD analyses for a comparison of further experimental studies. Furthermore, nitric oxide (NO) trapping capabilities of the free or supported nanoparticles and clean or strained (1%, 2%, 3%, 4% and 5%) MgO surfaces have been discussed.

Enhancing the tribological aspects of machining operation by hybrid lubrication-assisted side-flank-face laser-textured milling insert

The objective of this study was to find ways of improving the machining performance of steel considering sustainable development by limiting the application of hazardous lubricant. The textures having the most advantageous shape were employed with hybrid lubrication consisting of graphite powder in combination with the MQL having best suited tribo-rheological properties. The genetic algorithm code generated for optimization of the machining parameters was one of its kinds that could consider categorical factors along with the continuous factors for optimization. It was found that the optimum machining condition was the low depth of cut, textured flank face impregnated with graphite powder and boric acid-dissolved MQL. Waviness in shape and the rougher inner surfaces of the textures produced through laser beam machining were found to allow the insert to act as a self-lubricating insert satisfactorily throughout the machining operation. Reduction in surface roughness of 64.4% was observed with the proposed technique. Providing an extremely minimal amount of lubrication to the textured tool using the developed technique led to the improved machining performance through cleaner production aiming at omitting waste.

Cost Effective Design of Extra High Voltage Transmission Lines for Minimizing Transmission Congestion Problems

In Country like India having dense population, laying down new transmission lines is a great challenge. Also country being on fast track development mode, there is unceasing demand of electricity due to ever increasing industrialization. With advent of Extra and Ultra High Voltage transmission lines, corridor width for transmission lines can be reduced significantly. This paper describes utilization of a novel objective function for optimizing the structural design of Extra High Voltage transmission lines. Right of way for transmission lines are decided based on the effects of electromagnetic fields and corona effects in vicinity of the transmission lines. Objective function developed here takes into account the combined effects of electromagnetic fields to optimize design of transmission line tower structure. With compaction of towers further Right of way reduces leading to cost benefits in land procurement. A real time case of a transmission line situated in Gujarat is considered for validating the results of Optimization algorithm developed by authors. MATLAB has been used as programming platform for mathematical modeling as well as a platform for developing GUI that facilitates plotting of fields in vicinity of transmission lines in 2D and 3D environment. Genetic Algorithm coding is used to solve the optimization problem. Significant cost benefits and compactness in transmission tower structure is achievable as is clear from the results shown in this paper. This optimization algorithm can be extended for Ultra High Voltage lines also.

A Hyper Heuristics Technique for Data Partitioning and Scheduling to Heterogeneous Systems using Genetic Algorithm and Improved Particle Swarm Optimization

Development of the load partitioning for multiple round load distribution and effective scheduling of partitioned load to heterogeneous processor is primary goal of distributed and parallel system. In this paper, we propose hyper heuristics scheduling algorithm for load partitioning using genetic and improved particle swarm optimization techniques.A communication model is used to predict the optimal activation order, optimal number of processor and optimal number of rounds of the load. Heuristics Based Scheduling Algorithm is proposed using Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) to form Scheduling Solutions (heuristics algorithms) for large scale system with diversity operator as sequence dependent and sequence independent scheduling. For this solution, processing time of the entire processing load will be reduced. Hybrid Real Code genetic algorithm(HRGA) computes optimal activation order with cross over and mutation operator without considering the processor latency and different types of variation in the perturbation parameters. In order to optimize this issue, we utilizeImproved Particle swarm optimization (IPSO) determine the load fraction for generating activation order in terms of dynamically predicting fitness value of the processor with certain number. The Simulation analysis demonstrates the proposed model performance in terms of mean, standard deviation, computational complexity and Average Execution Time comparing against hybrid real coded genetic algorithm