Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Multi-objective optimization strategies using adjoint method and game theory in aerodynamics

There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

Multi-objective reentry trajectory optimization method via GVD for hypersonic vehicles

In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory optimization (MORTO) approach via generalized varying domain (GVD) is proposed. Using the direct collocation approach, the trajectory optimization problem involving multiple objectives is discretized into a nonlinear multi-objective programming with priorities. In terms of fuzzy sets, the objectives are fuzzified into three types of fuzzy goals, and their constant tolerances are substituted by the varying domains. According to the principle that the objective with higher priority has higher satisfactory degree, the priority requirement is modeled as the order constraints of the varying domains. The corresponding two-side, single-side, and hybrid-side varying domain models are formulated for three fuzzy relations respectively. By regulating the parameter, the optimal reentry trajectory satisfying priorities can be achieved. Moreover, the performance about the parameter is analyzed, and the algorithm to find its specific value for maximum priority difference is proposed. The simulations demonstrate the effectiveness of the proposed method for hypersonic vehicles, and the comparisons with the traditional methods and sensitivity analysis are presented.

Distributed Alternating Direction Method of Multipliers for Multi-Objective Optimization

In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.

A Study on the Multi-Objective Optimization Method of Brackets in Ship Structures

The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.

Optimization of Open-cast Mining Procedure Based on RSR Method

To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four representative mining procedures proposed by this mine.A detailed and comprehensive evaluation system is constructed using rank-sum ratio(RSR)method.The system covers 17 key indicators and aims to evaluate the advantages and disadvantages of each scheme in an all-round and multi-angle manner.Through the calculation and analysis by RSR method,the comprehensive evaluation of the four types of mining procedure schemes is carried out,and finally the secondary river improvement project is determined as the optimal mining implementation scheme,and the joint mining scheme of the south and north areas is the alternative strategy.The research results of this paper are objective,clear and definite,can not only reveal the effectiveness and feasibility of RSR method in solving the problem of open-cast mining procedure optimization,but also provide a strong technical support and decision-making basis for the future production development of Huolinhe No.1 Open-cast Mine.Thus,this study is expected to further promote the scientific and refined process of mining operations.

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.

Teaching evaluation on a WebGIS course based on dynamic self-adaptive teaching–learning-based optimization

Teaching evaluation on a WebGIS course is a multi-objective nonlinear high-dimensional NP-hard problem. The index system for the teaching evaluation of a WebGIS course, including teacher- and student-oriented sub-systems, is first established and used for questionnaires from 2013 to 2017. The multi-objective nonlinear high-dimensional evaluation model is constructed and then solved via dynamic self-adaptive teaching–learning-based optimization (DSATLBO). DSATLBO is based on teaching–learning-based optimization with five improvements: dynamic nonlinear self-adaptive teaching factor, extracurricular tutorship factor, dynamic self-adaptive learning factor, multi-way learning factor, and non-dominated sorting factor. WebGIS teaching performance is fully evaluated based on questionnaires and DSATLBO. Optimal weights and weighted scores from DSATLBO are compared with those from the non-dominated sorting genetic algorithm-II using the Pareto front, coverage to two sets, and spacing of the non-dominated solution sets to validate the performance of DSATLBO. The results show that DSATLBO can be uniformly distributed along the Pareto front. Therefore, DSATLBO can efficiently and feasibly solve the multi-objective nonlinear high-dimensional teaching evaluation model of a WebGIS course. The proposed teaching evaluation method can help reflecting the quality of all aspects of classroom teaching and guide the professional development of students.

Hybrid multi-objective optimization of microstructural and mechanical properties of B_4C/A356 composites fabricated by FSP using TOPSIS and modified NSGA-Ⅱ

A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial neural network(ANN)and non-dominated sorting genetic algorithm-II(NSGA-II).Firstly,microstructural properties of the composites fabricated in different processing conditions were investigated.Results show that FSP parameters such as rotational speed,traverse speed and tool pin profile significantly affect the size of the primary silicon(Si)particles of the base metal,as well as the dispersion quality and volume fraction of reinforcing B4C particles in the composite layer.Higher rotational to traverse speeds ratio accompanied by threaded pin profile leads to better particles distribution,finer Si particles and smaller B4C agglomerations.Secondly,hardness and tensile tests were performed to study mechanical properties of the composites.FSP changes the fracture mechanism from brittle form in the as-received metal to very ductile form in the FSPed specimens.Then,a relation between the FSP parameters and microstructural and mechanical properties of the composites was established using ANN.A modified NSGA-II by incorporating diversity preserving mechanism called theεelimination algorithm was employed to obtain the Pareto-optimal set of FSP parameters.

Multi-Objective Image Optimization of Product Appearance Based on Improved NSGA-II

A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product.First,the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users;then,the product multidimensional scale analysis is applied to classify the research objects,and again the reference samples are screened by the semantic differentialmethod,and the samples are parametrized in two dimensions by using elliptic Fourier analysis;finally,the fuzzy dynamic evaluation function is used as the objective function of the algorithm,and the coordinates of key points of product contours Finally,with the fuzzy dynamic evaluation function as the objective function of the algorithm and the coordinates of key points of the product profile as the decision variables,the optimal product profile solution set is solved by DNSGA-II.The validity of the model is verified by taking the optimization of the shape scheme of the hospital connection site as an example.For comparison with DNSGA-II,other multi-objective optimization algorithms are also presented.To evaluate the performance of each algorithm,the performance evaluation index values of the five multi-objective optimization algorithms are calculated in this paper.The results show that DNSGA-II is superior in improving individual diversity and has better overall performance.

Research on Spatial Optimization Level Strategy of Beijing Historical Blocks Based on Tourists’Cognitive Evaluation

Tourists,as the main beneficiary of historical block protection and tourism development,are considered qualified to speak on the matter of the preservation of space quality and regional characteristics.The study established a questionnaire using semantic analysis to investigate and evaluate tourists’ perspective of various spatial elements in Beijing’s three historical blocks.Factor analysis and IPA method was introduced to explore how various spatial elements affected the overall environmental perception of tourists.Based on the relative importance of spatial elements,suggestions for optimizing the space of historical blocks were proposed.The study found that:①The overall evaluation of various spatial elements of the historical block was relatively positive,and only the "environmental atmosphere" was closer to the negative evaluation,indicating that most tourists think the environment was too noisy;②All spatial elements can be classified into three indicators:"visual experience","street form" and "perception of regional characteristics";③Landscape planting and space interface needed to be improved first,followed by ground pavement,traffic organization,and advertisement signboard.The research provides a theoretical foundation for reducing the damage caused by improper tourism development behaviors,further improving the spatial quality of historical blocks,and enhancing tourists’ satisfaction of the historical environment.

Multi-Objective Incomplete Probability Information Optimization Reliability Design Based on Ant Colony Algorithm

In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.

Multi-objective reliability optimization design of high-speed heavy-duty gears based on APCK-SORA model

For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective.Based on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model.The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation algorithm.The objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.

Monte Carlo Method for the Uncertainty Evaluation of Spatial Straightness Error Based on New Generation Geometrical Product Specification

Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.

FAST COMPUTATION OF WIDEBAND RCS USING CHARACTERISTIC BASIS FUNCTION METHOD AND ASYMPTOTIC WAVEFORM EVALUATION TECHNIQUE

The Asymptotic Waveform Evaluation (AWE) technique is an extrapolation method that provides a reduced-order model of linear system and has already been successfully used to analyze wideband electromagnetic scattering problems. As the number of unknowns increases, the size of Method Of Moments (MOM) impedance matrix grows very rapidly, so it is a prohibitive task for the computation of wideband Radar Cross Section (RCS) from electrically large object or multi-objects using the traditional AWE technique that needs to solve directly matrix inversion. In this paper, an AWE technique based on the Characteristic Basis Function (CBF) method, which can reduce the matrix size to a manageable size for direct matrix inversion, is proposed to analyze electromagnetic scattering from multi-objects over a given frequency band. Numerical examples are presented to il-lustrate the computational accuracy and efficiency of the proposed method.

A multi-objective optimal PID control for a nonlinear system with time delay

It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design. @ 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1306306]

Novel electromagnetism-like mechanism method for multiobjective optimization problems

As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.

Application for Optimization of New Soft Magnetic Material Motor by Taguchi Method

The application of new soft magnetic materials in permanent magnet motor can effectively reduce the loss of motor and improve the efficiency of motor. Taguchi method is a local multivariable and multi-objective optimization method widely used in various engineering problems, which can effectively improve the efficiency of engineering optimization. In this paper, based on a 25 kW, 1700 r/min three-phase permanent magnet motor, the relevant motor model is established in the finite element simulation software, and the relevant simulation analysis is carried out. Combined with Taguchi method optimization, the local optimal structure scheme is obtained. Through optimization, the motor can maintain high efficiency, reduce the cogging torque of the motor by 53.45%, reduce the torque ripple by 36.79%, and increase the torque generated by the permanent magnet per unit mass by 21.42%. Through this optimization, the overall performance of the motor has been significantly improved. The research content of this paper verifies the feasibility of the application of Taguchi method in the optimization of new soft magnetic material motor, provides a new idea for the optimization design of new soft magnetic material motor, and also provides a certain reference for the local multi-objective optimization of the electromagnetic structure of other similar motors.

Multi-objective Evolutionary Algorithm Based on Target Space Partitioning Method

Considering the defects of conventional optimization methods, a novel optimization algorithm is introduced in this paper. Target space partitioning method is used in this algorithm to solve multi-objective optimization problem, thus achieve the coherent solution which can meet the requirements of all target functions, and improve the population's overall evolution level. The algorithm which guarantees diversity preservation and fast convergence to the Pareto set is applied to structural optimization problems. The empirical analysis supports the algorithm and gives an example with program.

An Enhanced Difference Method for Multi-objective Model of Cellular Base Station Antenna Configurations

In this paper, we propose a fine-grained grid-based multi-objective model which aims at optimizing base station antennas' configurations, such as transmit power, antenna tilt and antenna azimuth, in order to upgrading network performance in cellular networks. As the model is non-convex, non-smooth and discrete and computationally expensive, we use decomposition method to solve the MOP problem. We mainly focus on addressing the scalarized sub-problem after decomposition. For the scalarized sub-problem, we propose an enhanced difference method. First, difference of each component is calculated, which provides the guidance of optimization. Then an OPSO is applied to search the optimal step length. The method is applied to GSM network optimization on an area in Beijing. The effect of the application shows that proposed method has a good performance, and is effective/efficient to solve mobile network optimization problems.