Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization (MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process (IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization, though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior. © 2016, Tianjin University and Springer-Verlag Berlin Heidelberg.

A New Definition and Calculation Model for Evolutionary Multi-Objective Optimization

We present a new definition (Evolving Solutions) for Multi-objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary model (MINT Model) to solve MOPs. The new theory is based on our understanding of the natural evolution and the analysis of the difference between natural evolution and MOP, thus it is not only different from the Converting Optimization but also different from Pareto Optimization. Some tests prove that our new theory may conquer disadvantages of the upper two methods to some extent.

Aircraft Landing Gear Control with Multi-Objective Optimization Using Generalized Cell Mapping

This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.

Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

Research and Application of Pollution Control in the Middle Reach of Ashe River by Multi-Objective Optimization

Based on one-dimensional water quality model and nonlinear programming, the point source pollution reduction model with multi-objective optimization has been established. To achieve cost effective and best water quality, for us to optimize the process, we set pollutant concentration and total amount control as constraints and put forward the optimal pollution reduction control strategy by simulating and optimizing water quality monitoring data from the target section. Integrated with scenario analysis, COD and ammonia nitrogen pollution optimization wasstudiedin objective function area from Mountain Maan of Acheng to Fuerjia Bridge along Ashe River. The results showed that COD and NH3-N contribution has been greatly reduced to AsheRiverby 49.6% and 32.7% respectively. Therefore, multi-objective optimization by nonlinear programming for water pollution control can make source sewage optimization fairly and reasonably, and the optimal strategies of pollution emission are presented.

A branch-and-bound algorithm for discrete multi-factor portfolio optimization model

In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Coach Simplified Structure Modeling and Optimization Study Based on the PBM Method

For the coach industry, rapid modeling and efficient optimization methods are desirable for structure modeling and optimization based on simplified structures, especially for use early in the concept phase and with capabilities of accurately expressing the mechanical properties of structure and with flexible section forms. However, the present dimension-based methods cannot easily meet these requirements. To achieve these goals, the property-based modeling （PBM） beam modeling method is studied based on the PBM theory and in conjunction with the characteristics of coach structure of taking beam as the main component. For a beam component of concrete length, its mechanical characteristics are primarily affected by the section properties. Four section parameters are adopted to describe the mechanical properties of a beam, including the section area, the principal moments of inertia about the two principal axles, and the torsion constant of the section. Based on the equivalent stiffness strategy, expressions for the above section parameters are derived, and the PBM beam element is implemented in HyperMesh software. A case is realized using this method, in which the structure of a passenger coach is simplified. The model precision is validated by comparing the basic performance of the total structure with that of the original structure, including the bending and torsion stiffness and the first-order bending and torsional modal frequencies. Sensitivity analysis is conducted to choose design variables. The optimal Latin hypercube experiment design is adopted to sample the test points, and polynomial response surfaces are used to fit these points. To improve the bending and torsion stiffness and the first-order torsional frequency and taking the allowable maximum stresses of the braking and left turning conditions as constraints, the multi-objective optimization of the structure is conducted using the NSGA-II genetic algorithm on the ISIGHT platform. The result of the Pareto solution set is acquired, and the selection strategy of the final solution is discussed. The case study demonstrates that the mechanical performances of the structure can be well-modeled and simulated by PBM beam. Because of the merits of fewer parameters and convenience of use, this method is suitable to be applied in the concept stage. Another merit is that the optimization results are the requirements for the mechanical performance of the beam section instead of those of the shape and dimensions, bringing flexibility to the succeeding design.

Analyzing the Relation Between Earthquakes and Active Faults Based on the Parameter Optimization Model

The gestation and occurrence of strong earthquakes are closely related to fault activity, which is not only revealed by abundant experimentation and seismism but also proved by modern seismology. On the Chinese mainland, the relation between earthquake activity and active faults is one of the bases for partitioning potential seismic sources, analyzing the seismotectoulcs and estimating location of strong earthquakes.Due to the nonuniformity of earth media, instability of observation systems and disturbance of the environment, etc, the variety of observational data is complicated, that is, there is no absolutely ＂normal＂ or ＂abnormal＂, and seismic anomalies can be divided into many mutually exdusive＂ abnormal states＂. In different conditions of combined time-spacestrength, determining seismic anomalies by different monomial forecast methods and its efficiency could be different due to the uncertainty of a precursor itself or complexity of the relationship between a precursor and earthquake gestation. It is very difficult to discover and dispose of this difference in actual application in a ＂two-state＂ model. But in a ＂multi-state＂ model, the difference can be easily reflected and the optimal combination of forecasting parameters for a forecast method can also be determined easily. Based on the ＂multi-state＂ precursory model and the optimization method for parameters of earthquake forecast model under the condition of optimal forecast efficiency, the relationship of the spatial location of earthquake with M ≥ 6.0 and active faults in three seismic belts are analyzed. The results demonstrate that in the Hetao Seismic Belt, seismicity is mostly concentrated in the range of 20 km along the fault, the optimization model can forecast the location of potential earthquakes of M ≥ 6.0 near the faults with a relatively high accuracy and the reliability is 0.5 ; while in the Qilian Mt. Seismic Belt, the reliability only reaches 0.14 when we use the model to estimate earthquakes within 30 km range along the faults. The ＂multi-state＂ precursory model, the efficiency-evaluating model and the parameter selection of individual earthquake forecast model based on optimal efficiency are of certain revelatory and practicable meanings for developing knowledge about precursors, investigating the laws of earthquake preparation and searching for optimal forecasting methods.

Three-dimensional physical simulation and optimization of water injection of a multi-well fractured-vuggy unit

With complex fractured-vuggy heterogeneous structures, water has to be injected to facilitate oil pro- duction. However, the effect of different water injection modes on oil recovery varies. The limitation of existing numerical simulation methods in representing fractured- vuggy carbonate reservoirs makes numerical simulation difficult to characterize the fluid flow in these reservoirs. In this paper, based on a geological example unit in the Tahe Oilfield, a three-dimensional physical model was designed and constructed to simulate fluid flow in a fractured-vuggy reservoir according to similarity criteria. The model was validated by simulating a bottom water drive reservoir, and then subsequent water injection modes were optimized. These were continuous （constant rate）, intermittent, and pulsed injection of water. Experimental results reveal that due to the unbalanced formation pressure caused by pulsed water injection, the swept volume was expanded and consequently the highest oil recovery increment was achieved. Similar to continuous water injection, intermit- tent injection was influenced by factors including the connectivity of the fractured-vuggy reservoir, well depth, and the injection-production relationship, which led to a relative low oil recovery. This study may provide a constructive guide to field production and for the devel- opment of the commercial numerical models specialized for fractured-vuggy carbonate reservoirs.

Design of multi-layered porous fibrous metals for optimal sound absorption in the low frequency range

We present a design method for calculating and optimizing sound absorption coefficient of multi-layered porous fibrous metals （PFM） in the low frequency range. PFM is simplified as an equivalent idealized sheet with all metallic fibers aligned in one direction and distributed in periodic hexagonal patterns. We use a phenomenological model in the literature to investigate the effects of pore geometrical parameters （fiber diameter and gap） on sound absorption performance. The sound absorption coefficient of multi- layered PFMs is calculated using impedance translation theorem, To demonstrate the validity of the present model, we compare the predicted results with the experimental data. With the average sound absorption （low frequency range） as the objective function and the fiber gaps as the design variables, an optimization method for multi-layered fibrous metals is proposed. A new fibrous layout with given porosity of multi-layered fibrous metals is suggested to achieve optimal low frequency sound absorption. The sound absorption coefficient of the optimal multi-layered fibrous metal is higher than the single- layered fibrous metal, and a significant effect of the fibrous material on sound absorption is found due to the surface Dorosity of the multi-layered fibrous.

Application of multi-outputs LSSVR by PSO to the aero-engine model

Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.

Optimal Seat and Suspension Design for a Half-Car with Driver Model Using Genetic Algorithm

This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.

Bi-Criteria Optimization Technique in Stochastic System Maintenance Allocation Problem

In this paper, the problem of optimum allocation of repairable and replaceable components in a system is formulated as a Bi-objective stochastic non linear programming problem. The system maintenance time and cost are random variable and has gamma and normal distribution respectively. A Bi-criteria optimization technique, weighted Tchebycheff is used to obtain the optimum allocation for a system. A numerical example is also presented to illustrate the computational details.

Configurable Product Design Considering the Transition of Multi-hierarchical Models

The current research of configurable product design mainly focuses on how to convert a predefined set of components into a valid set of product structures. With the scale and complexity of configurable products increasing, the interdependencies between customer demands and product structures grow up as well. The result is that existing product structures fails to satisfy the individual customer requirements and hence product variants are needed. This paper is aimed to build a bridge between customer demands and product structures in order to make demand-driven fast response design feasible. First of all, multi-hierarchical models of configurable product design are established with customer demand model, technical requirement model and product structure model. Then, the transition of multi-hierarchical models among customer demand model, technical requirement model and product structure model is solved with fuzzy analytic hierarchy process （FAHP） and the algorithm of multi-level matching. Finally, optimal structure according to the customer demands is obtained with the calculation of Euclidean distance and similarity of some cases. In practice, the configuration design of a clamping unit of injection molding machine successfully performs an optimal search strategy for the product variants with reasonable satisfaction to individual customer demands. The proposed method can automatically generate a configuration design with better alternatives for each product structures, and shorten the time of finding the configuration of a product.

Uncertain and multi-objective programming models for crop planting structure optimization

Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.

Static Optimization of a Compliant Vertical Access Riser

The compliant vertical access riser (CVAR) is a new riser concept with good compliance;it can significantly reduce operating costs by eliminating the need for additional machines to operate wells directly on the platform.In this study,we determined the optimal riser parameters in terms of the stress and riser weight by optimizing the CVAR,and we compared the optimization resuits.A two-dimensional nonlinear static CVAR model was deduced according to the principles of virtual work and variation,and the model was verified using MATLAB.Design of experiments and Kriging method were used to reduce the number of sample calculations and improve the modeling accuracy.An appropriate selection of the multi-objective optimization problem (MOP) and the non-dominated sorting genetic algorithm helped to optimize the CVAR design.The non-dominated sorting genetic algorithm Ⅱ was used to solve the Pareto frontier of the optimization model in order to provide decision makers with more choices for the optimization results.After optimizing the riser parameters,the geometry of the riser was smoother,and the stress and stress differences were greatly reduced;the maximum equivalent stresses at the top and bottom were reduced by 36.6% and 44%,respectively.In addition,the stress difference in the buoyancy block area was reduced by 20.9%,and the weight of the riser was increased significantly by 28.1%.