Multi-objective Genetic Algorithm Method Research Articles

Optimization of the primary nozzle based on a modified condensation model in a steam ejector

The beauty and complexity of steam ejectors have fascinated scientists for decade. A modified condensation model is presented to evaluate the steam ejector performance and to optimize the steam ejector geometry. Four models consisting of different nucleation models and droplet growth models are employed to predict the non-equilibrium condensation phenomenon in a nozzle and in a steam ejector. The results show that the modified condensation model is more appropriate than the other three models. The internal flow structures as well as p-h diagram are applied in the steam ejector performance analysis. The results show that the steam ejector entrainment ratio predicted by the modified condensation model is smaller than the value predicted by the dry gas model. Given that the nucleation process mainly occurs in the steam ejector primary nozzle, only the primary nozzle is optimized using the Multi-Objective Genetic Algorithm method based on the modified condensation model. The optimal results indicate that the entrainment ratio of the optimized steam ejector increases by about 27.5% against the original value. The findings are of fundamental and practical relevance. It hopes that experimenters will be motivated to check our predictions.

Multi-objective topology optimization of two-dimensional multi-phase microstructure phononic crystals

The band gap (BGs) of a material can be manipulated by tailoring its periodic microstructure. The phononic crystals (PnCs), representative of a periodically arranged structural material, are conventionally manufactured from single-phase or two-phase materials. In this study, we investigate the multi-objective topology optimization (MOTO) of periodic three-solid-phase PnCs. With a method of two-stage multi-objective genetic algorithm, the microstructures of the three-solid-phase PnCs were designed to exhibit the desired novel properties. Three case studies were obtained as follows: (1) maximum relative band gap width (RBGW) with a minimum mass, (2) maximum absolute band gap width (ABGW) at two different specified frequencies, and (3) maximum ABGW at specified mid-frequency. All the cases were effective for the MOTO of PnCs, and the Pareto optimal solution set could give the balance of the different competed objectives. Further, the principal mechanisms responsible for the creation of BGs, whether Bragg scattering or local resonance, are discussed. Moreover, comparing with the normal 2-phase materials, suchlike a wider lower-frequency BGs, some important advantages unveiled with the appearance of the three-solid-phase material.

High Temperature Operation Clothing Design Based on Heat Conduction Equation

Based on the knowledge of heat and mechanics, the relationship between density, specific heat, thermal conductivity and thickness is analyzed by means of heat conduction equation. Under the condition of the change of working time and temperature, the optimal two-dimensional spline interpolation method is used to construct the corresponding functional relation of the temperature distribution field, and based on the numerical model of multi-zone and multi-layer heat transfer of the dummy in the equipment. The thermal physical parameters of each region are determined by experiments, and the equations of thermal equilibrium are established. Under the condition of the problem, the temperature change outside the human body is obtained, and then the temperature distribution outside the human body is obtained. Based on the above model, the experimental results show that the heat absorbed by the outside is consumed layer by layer in the process of heat transfer at high temperature. The following known temperature distribution, at a specific ambient temperature and working time length, the thickness of layer II to layer IV is obtained. By using multi-objective programming and genetic algorithm, the optimal solution is obtained by iterative approximation of the optimal solution for many times, and the final data is obtained by using the method of multi-objective programming and genetic algorithm.

The Optimum Cutting Condition when High Speed Turning of Aluminum Alloy using Uncoated Carbide

Objective of study reported in this paper is finding the optimum cutting condition respected to surface roughness (Ra), flank wear (VB) and tool life (TL) when high speed turning of aluminum alloy using uncoated carbide. Turning test was carried out by full factorial design where cutting speed (v), feed (f) and depth of cut (a) were determined as the independent variables. The high level values were set as v 1250 m/min, f 0.15 mm/rev, a 1.2 mm and for the low level were v 1000 m/min, f 0.12 mm/rev, a 1 mm. By multiple-linear regression method, the data of response variables resulted from the testing was used to developed 3 (three) mathematical models of Ra, VB and TL. Those models, φ(v, f, a), were then used as the fitness functions for finding the optimum cutting condition using Multi Objective Genetic Algorithm (MOGA) method. The results of study show that the optimum cutting condition for gaining the best roughness is at v 1000 m/min, f 0.12 mm/rev and a 1.2 mm. At that cutting condition, Ra and TL are recorded at 1.22 microns and 2.32 minutes, respectively.

Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network

In recent years, multi-objective evolutionary optimization algorithms have shown success in different areas of research. Due to their efficiency and power, many researchers have concentrated on adapting evolutionary algorithms to generate Pareto solutions. This paper proposes a new memetic adaptive multi-objective evolutionary algorithm that is based on a three-term backpropagation network (MAMOT). This algorithm is an automatic search method for optimizing the parameters and performance of neural networks, and it relies on the use of the adaptive non-dominated sorting genetic algorithm-II integrated with the backpropagation algorithm, being used as a local search method. The presented MAMOT employs a self-adaptive mechanism toward improving the performance of the proposed algorithm and a local optimizer improving all the individuals in a population in order to obtain better accuracy and connection weights. In addition, it selects an appropriate number of hidden nodes simultaneously. The proposed method was applied to 11 datasets representing pattern classification problems, including two-class, multi-class and complex data reflecting real problems. Experiments were performed, and the results indicated that the proposed method is viable in pattern classification tasks compared to a multi-objective genetic algorithm based on a three-term backpropagation network (MOGAT) and some of the methods mentioned in the literature. The statistical analysis results of the t test and Wilcoxon signed-ranks test also show that the performance of the proposed method is significantly better than MOGAT.

A Multi-Objective Task Reallocating Method in Complex Product Design Process Considering Design Changes

In the highly dynamic design environments, task allocation is subject to considerable design changes. The task reallocating to cope with various unexpected design changes becomes an increasingly important issue in the complex product design (CPD). In this paper, we propose a task reallocating model based on an adaptive multi-objective genetic algorithm and Tabu search (AMOGATS) method to study the dynamic task allocating procedure considering design changes, which integrates the advantages of adaptive genetic algorithm and Tabu search algorithm. Three objectives, i.e. completion time, robustness and stability, are considered simultaneously to measure the task allocation performance. A real example is employed to test and evaluate the performance of proposed method. The computational results imply that the proposed AMOGATS performs better than the heuristic algorithms available in the literature, which has more advantages on the convergence rate and running efficiency than the other algorithms, along with a better solution. This work provides a useful decision support to carry out the task reallocating with high levels of flexibility and efficiency during the process of CPD.

Identification of Three‐Dimensional Equivalent Material Properties for Laminated Disks Pack of Electric Machine Stators: Application in Reciprocal Compressors

Laminated structures can be represented computationally by the finite element method (FEM) using the homogenization procedure, which consists of the adjustment of equivalent orthotropic properties to a homogeneous structure. The application occurs in stators of electric machines composed of stacked laminated disks connected to each other through windings and other fastening components. This paper describes a method to the dynamic characterization of a typical laminated stator through the application of the homogenization technique to the magnetic core and consideration of the effect of winding contour conditions and screw joints. Two simplified three‐dimensional models for the stator were compared. The first considers the application of a typical tightening of the fastening screws and the presence of a homogeneous isotropic volume representing the winding. The second considers the effect of the boundary condition of the winding on the region of the teeth of the nucleus in order to reduce the degrees of freedom of the complete model. The coupling between the components is accomplished through the application of modal synthesis methods, which require the definition of the surfaces and the type of connection between the components. The obtainment of the set of equivalent orthotropic properties is based on the minimization of residues related to the difference between the natural and experimental frequencies in the range of 0 to 10 kHz. This was carried out using the multiobjective genetic algorithm (MOGA) method used in conjunction with commercial Ansys® software. Both models presented satisfactory experimental correlation. The simplified model demonstrated limitations of representativeness emphasized in specific frequency bands.

Multiobjective optimization to reconstruct biological networks

Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.

Analytical Method of Multi-Objective Genetic Algorithm with Multi-Objective Messy Genetic Algorithm in Satellite Image Segmentation

Analytical Method of Multi-Objective Genetic Algorithm with Multi-Objective Messy Genetic Algorithm in Satellite Image Segmentation

Multi-objective optimization of the apple drying and rehydration processes parameters

The aim of the paper is to investigate the effect of drying and rehydration parameters on the quality of rehydrated apples and to optimize these parameters based on quality of the rehydrated products. Hybrid artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) method were successfully developed to model, simulate, and optimize the drying and rehydration parameters. This method was applied to the apple tissue, where the simultaneous maximization of the volume ratio (VR) and water absorption capacity (WAC) and the minimization of colour difference (CD) and dry matter loss (DML) were considered. The range of drying and rehydration parameters was: 50-70°C (drying temperature, Td), 0.01-6 m×s-1(drying air velocity, V), and 20-95°C (rehydration temperature, Tr). Additionally, the mathematical formulas for determing the quality parameters of rehydrated apple were developed. The best solution has been found only for: CD, VR, WAC (50.1°C for Td, 0.03 m×s-1 for V, and 67.5°C for Tr). The values of CD, VR, and WAC were predicted as 1.20, 53.2% and 0.57 respectively. It has been observed that DML was always conflicting with other quality parameters.

Temperature control method optimization with global criterion genetic algorithm in thermal vacuum chamber for satellite testing

The proportional integral derivative (PID) controller that is adopted in the temperature control system of thermal vacuum chamber (TVC) is manually tuned has caused the temperature profile required more time to stabilize and fluctuate during satellite testing. Thus, other method is required to do the tuning of the PID controller. An optimization algorithm is an alternative method that can be applied to do the PID tuning and an optimized system can be developed. In this study, the optimization algorithm that is able to do the PID tuning for temperature control system is investigated in order to be implemented in the TVC's temperature control system. The genetic algorithm (GA) is found to be the suitable method that can be implemented as it is able to optimize the settling time and overshoot very quickly in temperature control system compared to other methods. However, due to more than one objective aimed in this study, the global criterion genetic algorithm (GCGA), a multi objective genetic algorithm (MOGA) method become the best approach to be chosen. Two models were designed using PID controller and GCGA-PID controller for the TVC's temperature control system. Simulation testing is done and the settling time and overshoot value are measured to compare both models. Analysis suggests that the optimization tuning by using GCGA method improves the settling time 30% better than using the PID controller alone. Meanwhile, in terms of overshoot, the performance is increased by almost 99.85%. By applying the optimization algorithm, the TVC's temperature control method can be enhanced during satellite testing compare to the current manually implementation. (C) 2017 The Authors. Published by IASE.

Multi-objective optimization of hot steam injection variables to control wetness parameters of steam flow within nozzles

Abstract The formation of liquid droplets in the low pressure steam turbines has devastating impacts on the turbine adiabatic efficiency and also causes the mechanical damage of blades due to the occurrence of severe erosion phenomenon. Previous investigations have shown that the injection of steam can decrease liquid mass fraction as well as the size of the averaged radius of droplets. To exploit the maximum potentials of this method, the optimization of injection variables is necessary. In the present study, the numerical solution of wet steam flow by the injection of hot steam within convergent-divergent nozzles together with a multi-objective genetic algorithm method are used to evaluate the appropriate injection parameters. It is concluded that to reduce liquid droplet size by 66% and liquid mass fraction by 13%, an injection steam flow rate of 4% of the main stream flow rate with a temperature 1.8 times of inlet steam temperature is required. Such a reduction of liquid droplet size has an enormous effect on lowering the erosion damages of blades. Furthermore, the injection drives the liquid droplets away from the solid boundaries, which is also expected to reduce the possible mechanical damages to the blades and the casings of turbine.

Modeling and multi-objective optimization of a modified hybrid liquid desiccant heat pump (LD-HP) system for hot and humid regions

A hybrid liquid desiccant-heat pump (LD-HP) system for cooling in hot and humid regions is modeled and optimized in this paper. This hybrid LD-HP system contained dehumidifying and cooling sections. The whole system was modeled and analyzed in four energy, exergy, economic and environmental aspects. Then the system was optimized using multi-objective Genetic Algorithm (GA) method. With two objective functions (total annual cost and exergy efficiency) and eight system design parameters the optimum values of design parameters were estimated. Results for our case study showed that the proposed optimized LD-HP system decreased the electricity consumption for 33.2% in comparison with that for an electrical HP system during seven months of operation in a year (18.9% due to using desiccant dehumidifying system and 81.1% due to using a heat exchanger instead of an electrical heater). This amount of lower electricity consumption also provided 1.85×105kgCO2/year lower CO2 production (33.2%) in comparison with that for a conventional HP system. The COP of LD-HP system at the optimum point was also about 4.83 (in comparison with 2.74 for the conventional case in which heat pump and electrical heater were used). Finally, added equipment to the traditional HP system (dehumidifier, regenerator, heat exchangers, pumps and fans) had 3.04 years payback period.

A new method in beam shaping: Multi-Objective Genetic Algorithm method coupled with a Monte-Carlo based reactor physics code

In this study, a new technique which uses Multi-Objective Genetic Algorithm method (NSGA-II) coupled with a Monte-Carlo based reactor physics code (MCNP) was proposed to shape the beams of neutron sources. The method was applied for the beam of Piercing Beam Port of the Istanbul Technical University TRIGA Mark II Research and Training Reactor. The beam was shaped according to the requirements of the Boron Capture Neutron Therapy (BNCT) application as described by IAEA technical documents. A set of optimized patterns, consisting of the spectrum shifter, filtering, and collimator sub-patterns, were obtained. Epithermal neutrons were considered for BNCT.With the optimized pattern, the epithermal neutron flux at the port exit is calculated as 4.17 ± 0.14 × 108 n·cm−2·s−1 with a current-to-flux ratio of 0.87 ± 0.03. Thermal and fast neutron fluxes and photon flux are reduced by about 20, 3, and 200 times, respectively, when compared with the bare port values. As a result, BNCT is applicable in this port as the desired neutron fluxes at the port exit are obtained. The method suggested in this study provides promising results and is quite effective in shaping the beam.

Reducing emissions by optimising the fuel injector match with the combustion chamber geometry for a marine medium-speed diesel engine

The effects of seven matching parameters of a fuel injector and combustion chamber geometries on nitrogen oxide (NOx), soot and specific fuel oil consumption (SFOC) were investigated by means of a parametric study. The study was carried out on four different engine loads, i.e. L25 (25%), L50 (50%), L75 (75%) and L100 (100%) loads. The injection-related parameters were found to have more prominent influences as opposed to the combustion chamber geometries. Then, a multi-objective genetic algorithm (MOGA) method was proposed in order to identify a set of optimal designs for the L100 load. The emissions and performance of these optimal designs were also examined and compared on the other three engine loads. Finally, an optimal design which meets the IMO (International Maritime Organization) Tier II NOx emissions regulations (research shows it is impossible to meet Tier III NOx emissions regulations solely on the basis of the optimisation of the combustion progress) and which has the best fuel economy was singled out.

Magnetron Injection Gun Design for Multifrequency Band Operations

In this paper, a novel triode-type magnetron injection gun (MIG) with three emitters designed for gyrotron traveling wave amplifiers has been presented. This MIG can be operated in Ku/Ka/Q single band or Ku-Ka/Ka-Q dual band. The optimization of the MIG is accomplished by a multiobjective genetic algorithm method. It is anticipated that this MIG possesses an axial velocity spread of <5% in all the operating frequency bands. The results of sensitivity study show that this MIG can work stably within reasonable parametric windows. Velocity spreads considering cathode surface roughness and initial thermal temperature are analyzed. Cathode configuration with heat insulation chambers is introduced to cut off the thermal flow among the emitters. Thermal analysis has been performed.

Multiobjective Optimization Problem of Dieless Incremental Forming

Single Point Incremental Forming (SPIF) technology has been announced in the recentpast to manufacture sheet metal products by using Computer Numerical Control machines (CNC). Ithas been frequently used in different fields like the aeronautics. In incremental forming, materialsare submitted to permanent deformation by cold forming to produce a variety of three complicateddimensional shapes. The final form of the parts in sheet metal forming is highly affected by thespring-back and the pillow effect, occurring when the material is set free of the forming constraints.In this sense, the best solution is to adopt a process of multiobjective optimization in which a set ofnumerical simulations can be achieved on the basis of the box-Behnken experimental design. In thisway, the design variables are wall angle, initial thickness, tool diameter and incremental size. Tostudy the geometric characteristics, a cone-shaped part with circular base is considered. This paperaims to identify an overview of multiobjective design optimization of incremental metal formingparameters in order to minimize objective functions of pillow effect, springback and thinning ratesimultaneously. In an attempt to solve fitness functions, the method of Multiobjective GeneticAlgorithm (MOGA) is developed in this investigation. In this case, we should consider severalpoints of the appropriate process parameters which correspond to the best compromises with respectto several antagonistic objectives. As well as, a generation of the approximate Pareto optimalsolutions is presented in this study.

Energy optimization of the fin/rudder roll stabilization system based on the multi-objective genetic algorithm (MOGA)

Energy optimization is one of the key problems for ship roll reduction systems in the last decade. According to the nonlinear characteristics of ship motion, the four degrees of freedom nonlinear model of Fin/Rudder roll stabilization can be established. This paper analyzes energy consumption caused by overcoming the resistance and the yaw, which is added to the fin/rudder roll stabilization system as new performance index. In order to achieve the purpose of the roll reduction, ship course keeping and energy optimization, the self-tuning PID controller based on the multi-objective genetic algorithm (MOGA) method is used to optimize performance index. In addition, random weight coefficient is adopted to build a multi-objective genetic algorithm optimization model. The objective function is improved so that the objective function can be normalized to a constant level. Simulation results showed that the control method based on MOGA, compared with the traditional control method, not only improves the efficiency of roll stabilization and yaw control precision, but also optimizes the energy of the system. The proposed methodology can get a better performance at different sea states.

Finding optimum range for increasing the transport coefficients in a three-way catalytic converter in order to achieve minimum light-off time and maximum increase in efficiency using multiobjective genetic algorithm

In this article, an optimum range for increasing transport coefficients with respect to the laminar flow condition in the channel of a three-way catalyst is introduced. A mathematical model of a three-way catalyst was first developed. The model is a one-dimensional plug flow model which involves mass and energy equations in gas and solid phases. To save numerical time, the three-way catalyst model was replaced with a neural network model used in optimization which was solved by using a multi-objective genetic algorithm method. The results of optimization introduce a range in which the desirable objectives will be satisfied. The objectives are to achieve minimum light-off time and maximum increase in catalyst efficiency during different operating conditions of engine. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 368–379, 2015

Multiobjective approach developed for optimizing the dynamic behavior of incremental linear actuators

Purpose – The purpose of this paper is to develop an optimal approach for optimizing the dynamic behavior of incremental linear actuators. Design/methodology/approach – First, a parameterized design model is built. Second, a dynamic model is implemented. This model takes into account the thrust force computed from a finite element model. Finally, the multiobjective optimization approach is applied to the dynamic model to optimize control as well as design parameters. Findings – The Pareto front resulting from the optimization approach (or the parallel optimization approach,) is better than the Pareto, which is obtained from the only application of MultiObjective Genetic Algorithm (MOGA) method (or parallel MOGA with the same number of optimization approach objective function evaluations). The only use of MOGA can reach the region near an optimal Pareto front, but it consumes more computing time than the multiobjective optimization approach. At each flowchart stage, parallelization leads to a significant reduction of computing time which is halved when using two-core machine. Originality/value – In order to solve the multiobjective problem, a hybrid algorithm based on MOGA is developed.