Multiple-objective Optimization Method Research Articles

Build orientation optimization of additive manufactured parts for better mechanical performance by utilizing the principal stress directions

As Additive Manufacturing technology is excellent for the production of function-based optimized parts. By choosing the right printing orientation, the dimensional accuracy and surface quality of the parts can be improved. But also possible to achieve improvement of the mechanical properties by the proper orientation. An algorithm has been developed, which can determine the optimal build orientation based on a numerical simulation for a given load case. The adverse and favourable load directions can be defined according to the layer's position by knowing the anisotropic behavior of the printed parts. The optimal print orientation has been found for four investigated geometries, according to the No-Preference and Weighted Sum multiple-objective optimization methods, and therefore the expected mechanical performance was increased. The algorithm was able to reduce the amount of unfavourable stresses by 100 % for simple beam geometries with longitudinal tensile and compression loads, while the more complex geometries were improved to the best possible extent.

Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization

The energy consumption in buildings is increasing, then, a new global multiple-objective optimization method for zero-energy buildings with the comprehensive assessment method is used in this study. The applied optimization design methods for renewable energy systems are the multiple-objective optimization by Enhanced Archimedes Optimization Algorithm (EAOA) on the original design for energy costs and energy optimal models. The Hybrid Gray Multiple-Level Comprehensive Assessment Method (HGMLCAM) is utilized to the optimal model including the solar energy use efficacy, energy conservation, economic and social factors. By the selected decision based on the global optimal model, the best solution is presented. For designing the practical building, the defined solution can be useful for the architects and decision-makers. Consequently, a multiple-objective design for a building located in Shanghai, in China, is studied. Based on the results, it can be concluded that the presented method is efficient to make decisions.

Study on milling technology of Waspaloy superalloy internal threads with a small diameter

Small-diameter thread is widely used in the connection of aeroengine, nuclear equipment, and petrochemical equipment parts due to its advantages of high connection strength, simple structure, and convenient loading and unloading. Manual tapping is its main production method. But, the stability of product quality is poor; the qualified rate of one-time machining is less than 20%, resulting in a large number of repeated machining and scrap. The existing thread milling method is difficult to adapt to the small-diameter internal thread processing of Waspaloy high-temperature alloy. The deformation of the tool during the processing and the difficulty in heat dissipation from the unreasonable processing technology are the main reasons for the processing accuracy not meeting the standard. In this paper, a 3-D numerical simulation model of small-diameter internal thread milling is established based on the flow stress control theory. The influence of structure parameters and milling machining parameters on milling force and milling temperature is studied by orthogonal experiment, and the range analysis is carried out. The mathematical model of milling force and temperature is calculated by nonlinear regression analysis. Based on Pareto genetic algorithm, multiple objective optimization method, the cutter structure, and machining parameters are optimized. The better machining parameters and cutting cutter parameters are obtained; the deformation of thread milling cutter in X, Y, and Z direction is obtained, and a new structure of thread milling cutter is designed; the feasibility of the new type of thread milling cutter and the machining scheme is verified by the trial cutting experiment of the thread milling cutter.

Optimal design of a proton exchange membrane fuel cell-based combined cooling, heating, and power system by an enhanced version of farmland fertility optimizer

ABSTRACT A multiple-objective optimization method has been suggested for a combined cooling, heating, and power (CCHP) system in this study. The combined CHP system includes a heat-recovery system, a humidifier, a gas compressor, and a small size absorption chiller along with a 5-kilowatts Proton Exchange Membrane (PEM) Fuel Cell stack. An enhanced version of Farmland Fertility Optimizer has been suggested for developing the system efficiency concerning economics, environment, and thermodynamics. For system efficiency validation, it has been analyzed in terms of exergy and energy effectiveness, pollutant emission decline, and yearly expense and a comparison of the results with NSGA-II from the literature, and the basic FFO algorithm is carried out. The results show that the maximum value for the exergy efficiency and the annual GHG reduction is 48.08% and 5.45e7 g, respectively. Also, the annual cost has been decreased to 28.30e3 $. Simulation results show 35.12% energy effectiveness which is better than the compared methods.

Progress in thermodynamic analyses and optimizations for key component units in sea-based fuel synthesis systems

This paper provides a brief introduction to the finite-time thermodynamic studies performed for chemical reaction processes. The research progress in the thermodynamic analyses and optimizations of the key component units in the two subsystems of the sea-based fuel synthesis system are described. This paper describes the component units in the carbon- and hydrogen-capture subsystems (i.e., the modules on the electrochemical acidification of seawater, extraction of CO2 in the acidic seawater, and production of hydrogen). This paper also describes the component units in the catalytic synthesis subsystems, which include the reverse water gas shift unit reactor and the Fischer-Tropsch synthesis unit reactor in the fuel synthesis technological pathway of long-chain hydrocarbon cracking; the subsystem also includes the CO2 hydrogenation of light olefins unit reactor and the olefins oligomerization unit reactor in the fuel synthesis technological pathway of short-chain hydrocarbon polymerization. Modeling and performance optimizations were performed, and the thermal design and optimization for the key component units of the sea-based fuel synthesis system were explored by combining the thermodynamics, heat transfer, fluid dynamics, chemical reaction kinetics, optimal control theory, and multiple-objective optimization methods. From shallow to deep, a one-dimensional plug-flow reactor model (with a completely temperature-controlled heat reservoir) and a two-dimensional pseudo-homogeneous reactor model (with a real heat reservoir) were established; high-temperature helium was employed as the heat carrier. From the reversible to irreversible models, the classical equilibrium thermodynamic model and finite-time thermodynamic model that considers various irreversible factors were established. From a single objective to multiple-objective optimizations, the optimizations were performed for single objectives, including the minimum entropy generation rate and minimum specific entropy generation rate (i.e., the entropy generation rate averaged by the production rate of target products), and multiple-objectives by comprehensively considering the production rate of the target products’ maximization and entropy generation rate minimization. The outlet mean conversion rate maximization and maximum radial temperature difference minimization were performed. Some results with important theoretical significances and application values were obtained, which can provide the scientific bases and theoretical guidelines for the design and optimization for the sea-based fuel synthesis systems. One of the important developmental trends was the constructal thermodynamic optimization for the two subsystems and the integration of the full sea-based fuel synthesis system with the component units.

Multi-Objective Optimization of Cost Saving and Emission Reduction in Blast Furnace Ironmaking Process

Due to the increasing environmental pressures, one of the most direct and effective way to achieve emission reduction is to reduce the CO2 emissions of the blast furnace process in the iron and steel industry. Based on the substance conservation and energy conservation of ironmaking process and the engineering method, the carbon loss model was firstly established to calculate the amount of solution loss. Based on this model, the blast furnace emission reduction optimization mathematical model with the cost and CO2 emissions as objective functions was then established using the multiple-objective optimization method. The optimized results were obtained by using the GRG (Generalized Reduced Gradient) nonlinear solving method. The optimization model was applied to the B# blast furnace of BayiSteel in China. The optimization model was verified by comparing the optimized results with the actual production data. The optimization model was then applied to analyze the effects of coke ratio, coal rate, blast temperature and other factors on the cost, CO2 emission and solution loss, and some measures to save cost, reduce emissions and reduce solution loss have been proposed.

Constrained Multiobjective Nonlinear Optimization: A User Preference Enabling Method.

A novel user preference enabling (UPE) method is developed to solve general constrained nonlinear multiple objective optimization (MOO) problems. User wish lists on the preferred range of each objective function are introduced and incorporated into the MOO formulation to form a user-preferred (UP) MOO problem. A theoretical foundation of the UP feasible region of MOO problems is developed. The developed theoretical work leads to the development of a UPE method for effectively solving the UP-MOO problems. Distinguishing features of the proposed method include its ability to compute a targeted Pareto solution and to serve as a complement to existing MOO methods, in the sense that the proposed method assists the existing MOO methods in computing the Pareto front by providing feasible solutions and UP feasible solutions. Both proposed UPE method and derived theoretical developments are evaluated on several test systems with promising results.

多目的最適化手法を用いた構造物の動的問題における実験同定

Dynamic FEM analysis is valid for designing rotating machinery to reduce its vibration problem when we may ensure enough accuracy of the analysis. Surrogate multiple objective optimization method is one of the most effective methods for structural identification improving the FEM analysis model of a structure to adjust the natural frequency analysis results to the experimental results. In this study the structural identification method is applied to stator end-windings of a turbine generator to determine the Young's modulus of the principal components on the FE model minimizing the analysis errors of the natural frequencies of the 2-lobe and 4-lobe circular modes to the corresponding experimental results. The accurate FE model of the end-windings is obtained by this method.

Novel multi-objective optimization method of electric and magnetic field emissions from double-circuit overhead power line

Summary This paper proposes a novel method for determining optimal arrangements of overhead double-circuit power line conductors aimed to reduce electric and magnetic field emissions. Extremely low frequency electric and magnetic field optimization problems have multiple objectives. Multiple-objective optimization allows the simultaneous optimization of two or more conflicting objectives and determination of mutually non-dominated solutions. The multi-objective optimization technique used in this paper is based on Genetic Algorithm to obtain a set of mutually non-dominated solutions (Pareto optimal set) for the two objectives: the electric and magnetic field strengths near overhead double-circuit power line. Also, additional assessment criteria were defined for the selection of several decision scenarios from the Pareto optimal set. The results and practical aspects of the proposed methodology are demonstrated on two typical 400-kV double-circuit overhead power lines. The efficiency of the proposed multiple-objective optimization method is compared with the existing one based on single-objective optimization function. Copyright © 2016 John Wiley & Sons, Ltd.

Compounding problem in an interactive multiple objective optimization method

This note finds and resolves a compounding problem in the interactive multi-objective optimization method developed by Park and Shin (2012). The compounding problem means that the algorithm generates a solution unexpected by the decision maker. A concrete example of the compounding problem is demonstrated and, to remove the problem, an improved algorithm is then developed. We also show that the improved algorithm converges to the known optimal solutions faster than the earlier algorithm.

Robust design of Mars entry micro-probe with evidence theory and multi-fidelity strategies

Purpose – A multi-disciplinary robust design optimization method for micro Mars entry probe (no more than 0.8 m in diameter) is proposed. The purpose of this paper is to design a Mars entry probe, not only the geometric configuration, but the trajectory and thermal protection system (TPS). In the design optimization, the uncertainties of atmospheric and aerodynamic parameters are taken into account. The probability distribution information of the uncertainties are supposed to be unknown in the design. To ensure accuracy levels, time-consuming numerical models are coupled in the optimization. Multi-fidelity approach is designed for model management to balance the computational cost and accuracy. Design/methodology/approach – Uncertainties which cannot defined by usual Gaussian probability distribution are modeled with degree of belief, and optimized through with multiple-objective optimization method. The optimization objectives are set to be the thermal performance of the probe TPS and the corresponding b...

Multiple Objective of Train Operation Process Based on Modified Particle Swarm Optimization

According to the problem that train operation process costs much more energy in rail transit. Multi-objective optimization model is formulated on multiple objective optimization methods. The model aims to energy-efficient, punctuality and stopping at adequate position. In order to solve the basic PSO relapsing into local extremum and slow convergence in the late evolutionary, a method is put forward to improve the acceleration coefficient of PSO. Experiments result shows that the modified algorithm can get a more accurate global optimal value.

Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell

BackgroundHematopoiesis is a highly orchestrated developmental process that comprises various developmental stages of the hematopoietic stem cells (HSCs). During development, the decision to leave the self-renewing state and selection of a differentiation pathway is regulated by a number of transcription factors. Among them, genes GATA-1 and PU.1 form a core negative feedback module to regulate the genetic switching between the cell fate choices of HSCs. Although extensive experimental studies have revealed the mechanisms to regulate the expression of these two genes, it is still unclear how this simple module regulates the genetic switching.MethodsIn this work we proposed a mathematical model to study the mechanisms of the GATA-PU.1 gene network in the determination of HSC differentiation pathways. We incorporated the mechanisms of GATA switch into the module, and developed a mathematical model that comprises three genes GATA-1, GATA-2 and PU.1. In addition, a novel multiple-objective optimization method was designed to infer unknown parameters in the proposed model by realizing different experimental observations. A stochastic model was also designed to describe the critical function of noise, due to the small copy numbers of molecular species, in determining the differentiation pathways.ResultsThe proposed deterministic model has successfully realized three stable steady states representing the priming and different progenitor cells as well as genetic switching between the genetic states under various experimental conditions. Using different values of GATA-1 synthesis rate for the GATA-1 protein availability in the chromatin sites during the time period of GATA switch, stochastic simulations for the first time have realized different proportions of cells leading to different developmental pathways under various experimental conditions.ConclusionsMathematical models provide testable predictions regarding the mechanisms and conditions for realizing different differentiation pathways of hematopoietic stem cells. This work represents the first attempt at using a discrete stochastic model to realize the decision of HSC differentiation pathways showing a multimodal distribution.

Interactive multiobjective optimization approach to the input–output design of opening new branches

We demonstrate a real-world application of the interactive multiple objective optimization (MOO) approach to the simultaneous setting of input and output amounts for the opening of new branches. As illustrated by the case example, all the branches of a fast-food company employ multiple inputs to generate multiple outputs. The company launches several new branches each year and, therefore, needs to plan the quantities of inputs and outputs to be used and produced before their operations. Such input–output settings are a vital practical problem that arises whenever a new branch is opened in a host of different industries. In this paper, we show in detail the entire process of the application from modeling the case problem to generating its solution. In the modeling stage, a data envelopment analysis model and a statistical method are subsequently utilized to form a nonlinear MOO problem for the input–output settings. To solve this problem, we then develop and apply an interactive MOO method, which combines the two earlier interactive methods (Geoffrion et al., 1972; Zionts and Wallenius, 1976), while compensating for their drawbacks and capturing their positive aspects.

Operating-State-Based Intelligent Control of Combustion Process of Coke Oven

This paper describes a hierarchical intelligent integrated control method for controlling the combustion process in a coke oven. A key feature is the determination of the operating state of the oven from an analysis of the characteristics of the process. The system contains three layers: a decision layer, a temperature optimization and control layer, and a process control layer. An information fusion method is used in the decision layer to determine the operating state of the combustion process. The control strategy uses an outer and an inner loop. The outer loop uses fuzzy controllers to adjust the temperature, and to generate proper settings for the gas flow rate and air suction power for the inner loop. The parameters of the outer loop controllers are tuned by a multiple-objective optimization method with an adaptive genetic algorithm. The inner loop controllers keep the temperature of the coke oven in the proper range. A switching control strategy is used to select a suitable controller for the current operating state.

Function-based design synthesis approach to design reuse

In this paper, a design reuse framework with a function-based design synthesis approach is proposed in the context of conceptual product development. Previous researches in design reuse have lacked a comprehensive functional base for knowledge representation and reasoning. The method presented in this paper uses a function-based product information model and a multiple objective optimization model to achieve design reuse. The information model is dependent on a functional core which is the key element vector. It is capable of modeling product information with sufficient abstraction, which in turn facilitates intelligent construction of product platforms. The multiple objective optimization method carries out automated design synthesis and evaluation subject to various design constraints. The approach has been applied in the design of the fan filter unit, a key clean room device. It has achieved intelligent design reuse in product conceptual design with significant rapidity and solution variety.

An adaptive weighting strategy for microwave imaging problems

In the framework of microwave imaging applications, an innovative strategy aimed at addressing the multiobjective inverse scattering problem is proposed. Starting from the spatial-domain integral formulation, the original multiobjective problem is recast into a single-objective one by defining a suitable cost function as a linear combination of the data and state terms according to variable weighting parameters. By iteratively tuning these parameters, the optimization procedure is forced to solve an "almost" multiobjective problem avoiding the use of ad-hoc multiple-objective optimization methods and satisfying different objectives in a balanced way. Selected numerical results indicate that the use of such a strategy yields to accurate reconstructions, with noise-corrupted data as well, by improving the performances of the adopted optimization procedure.

Multiple Objective Test Assembly Problems

Mathematical programming techniques for optimal test assembly are discussed. Most methods optimize a single objective: for instance, the amount of information in a test, subject to a number of constraints. However, some test assembly problems have multiple objectives. A recent example in the literature is the problem of assembling test that measure multiple traits, where the amount of information in the test about each different trait has to be maximized. The present paper proposes methods appropriate for solving multiple objective test assembly problems. An overview of multiple objective optimization methods is given. The impact of the method on the optimality of the solution is shown and the appropriateness of the methods is discussed. The methods are illustrated using an empirical example of a test assembly problem for a two‐dimensional mathematics item pool.

Mechanical behavior of U-shaped bellows and shape optimal design using multiple objective optimization method

A bellows is frequently applied in piping systems for absorbing mechanical movement. Its geometry is an axial symmetric shell, which is composed of two toroidal shells and one annular plate. The mechanical behavior of U-shaped bellows under axial force and internal pressure is estimated by changing the dimensions of the geometric parameters. The changing ranges of the geometric dimensions is so selected as to invest the results with practical environments in many fields. The minimization of strength and spring rate is considered simultaneously as a multiple objective function. The weighting objective method is implemented, in which a vector function is transformed to a scalar function. The structure is analyzed by the energy method for toroidal sections. Optimization is carried out by the Recursive Quadratic Programming algorithm.

Multiple-objective optimization by a multiplier method of proper equality constraints--Part I: Theory

Lin [1] has shown that general multiple-objective (MO) optimization problems can be solved by transforming them into single-objective (SO) optimization problems with all but one of the multiple objectives converted to proper equality constraints. This paper continues the development of such a general-purpose MO optimization method. It is shown here that Lagrange multipliers, which are intermediate by-products in the process of solving SO constrained optimization problems (by analytical or numerical methods), can be effectively utilized for testing/verifying whether the objective-converted equality constraints are proper or not. Several useful necessary and sufficient conditions for properness are derived and expressed in terms of Lagrange multipliers. These necessary and sufficient conditions are derived using no convexity or concavity assumptions. They are suitable for general analytical characterization of Pareto-optimal solutions, as well as for numerical generation of Pareto-optimal solutions by penalty methods, primal-dual methods, and Hestenes-Powell-Rockafellar methods of multipliers.