Passive designs and strategies for low-cost housing using simulation-based optimization and different thermal comfort criteria

An optimum design of low-cost housing offers low-income urban inhabitants great opportunities to obtain a shelter at an affordable price and acceptable indoor thermal conditions. In this paper, the design and operation of a low-cost dwelling were numerically optimized using a simulation-based approach. Three multi-objective cost functions including construction cost, thermal comfort performance and 50-year operating cost were applied for naturally ventilated and air-conditioned buildings. Thermal environment inside the house was controlled and assessed by two thermal comfort models. Optimization problems which consist of 18 design parameters and 6 ventilation strategies were examined by two population-based probabilistic optimization algorithms (particle swarm optimization and hybrid algorithm). Optimum designs corresponding to each objective function, differences in optimal solutions, energy saving by the adaptive comfort approach and optimization effectiveness were outlined. The optimization method used in this paper shows a considerable potential of comfort improvement, energy saving and operating cost reduction.     

Optimal design method for building energy systems using genetic algorithms

In this paper, a new optimal design method for building energy systems is proposed. This method provides the most efficient energy system, best combination of equipment capacity and best operational planning for cooling, heating, and power simultaneously with respect to certain criteria such as energy consumption, CO₂ emission, etc. Specifically for this paper, the authors apply this method to a sample building as a case study. The “Genetic Algorithms (GA)” optimization method, which can resolve nonlinear optimization problems, is adopted for this optimization analysis. Also its applicability is analyzed in a case study. In order to validate the accuracy of this method, the correct optimum solution based on comprehensive inquiries is also calculated. A comparison of the GA solution with the correct solution demonstrates fairly good agreement. The results show that the proposed method is sufficiently capable of determining the optimal design and has the potential to be applied to very complex energy systems with appropriate modifications.     

基于快速群搜索算法的双层球面网壳多目标优化

提出一种新的群智能算法,即多目标快速群搜索优化（MQGSO）算法,以双层球面网壳为研究对象,进行结构节点最大位移最小化和结构质量最轻化的多目标优化设计,网壳优化后的Pareto前沿分布均匀且范围较广,分别对多目标优化和单目标优化后的结构进行多维地震作用时程响应分析。结果表明:多目标优化得到的结构优于单目标的优化结果,所提出的多目标快速群搜索算法能很好地实现复杂空间结构的多目标优化设计。     

Post-Pareto optimality approach to enhance budget allocation process for bridge rehabilitation management

A variety of conflicting criteria in the form of objective functions exist in budget allocation optimisation problem for bridge rehabilitation projects. Budget allocation decision-making for such transportation assets is generally a combinatorial problem. The nature of the problem is a good reason for decision-makers to apply multi-objective optimisation techniques. However, manually choosing an acceptable solution from a set of optimal solutions is a time-consuming task, which would be avoided if the optimisation technique could be followed by a ranking method to obtain unique acceptable solution. To enhance the budget allocation process, this paper develops a posteriori approach to prioritise Pareto-Optimal (PO) solutions generated by genetic algorithm in order to identify a unique package of bridge rehabilitation activities. By identifying the most conventional objective functions for bridge rehabilitation based on technical and managerial criteria, a multi-objective knapsack problem is constructed. PO solutions will then be prioritised applying ‘Technique for Order Preference by Similarity to an Ideal Solution’. The feasibility of the study will be finally demonstrated through an illustrative example. The proposed ranking approach may facilitate the budget allocation optimisation process for bridge rehabilitation where one or a few acceptable solutions are demanded.     

Fuzzy-based MOGA approach to stochastic time–cost trade-off problem

In construction projects, time and cost are manageable objectives with significant interdependencies for which sets of trade-offs may exist. This study presents a new approach for the solution of time–cost trade off problems in an uncertain environment. Fuzzy numbers are used to address the uncertainties in the activities execution times and costs. Fuzzy sets theory is then explicitly embedded into the optimization procedure. A multi-objective genetic algorithm is specially tailored to solve the discontinuous and multi-objective fuzzy time- cost model with relatively large search space. The proposed approach identifies the best set of implementation options defined by the sets of non-dominated solutions Accepted risk level and optimism of the decision maker are addressed using α-cut approach and optimism index (β) respectively. To illustrate the application and performance of the model, two case examples are presented, for which separate Pareto fronts are developed. The fuzzy presentation of the non-dominated solution helps the project manager to apply his own level of risk acceptance and degree of optimism in decision making process. Different risk acceptance level and/or optimism leads to different scheduling and sets of Pareto solutions from which the project manager may select his preferred options.     

基于遗传算法的飞艇结构多目标优化

首先,根据软式飞艇结构的力学特点建立了飞艇结构的多目标优化模型,以外荷载作用下飞艇“应力最小”、“变形最小”及“自重最小”为优化目标,确定膜材厚度、膜材弹性模量、飞艇内气压及飞艇细长比为优化变量.其次通过加权系数法将多目标优化转化为单目标问题,并采用遗传算法进行求解.最后,采用此方法对某一飞艇结构进行多目标优化.研究结果表明：飞艇的优化模型是有效的;结合加权系数法和遗传算法,可以获得各个目标均较理想的优化结果;通过进行多目标优化可以为飞艇结构的设计提供合理建议.     

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.     

Construction cost and energy performance of single family houses: From integrated design to automated optimization

The single family home market is facing increasing challenges in managing environmental issues. The required objective of building energy performance can be achieved by limiting extra cost, integrating building design, and using the most appropriate and readily available materials. However, standard computations, such as the French building energy code used here, require vocational expertise that involves managing separate processes and numerous design variables. The design is therefore restricted to well-known techniques, especially for small constructions. In this paper, the usual stakeholder constraints and possible developments in design practice are considered through the use of real product databases and vocational tools to calculate construction costs. In the first stage, which takes into account cost and energy demand, an integrated approach to building envelope design is detailed, including a semantic system to automate the process. Then an optimization method (a genetic algorithm) is proposed to assess energy performance and the cost of the building envelope. This process is illustrated as a case study for a single family house. The results highlight various optimal solution domains specific to the case study, which can be further managed through a decision support system.     

基于遗传算法的密肋复合墙体多目标优化设计方法研究

密肋复合墙体是密肋壁板结构体系的主要受力构件,墙体的优化设计方法研究是结构体系设计理论的重要组成部分。在前期研究的基础上,建立综合考虑密肋复合墙体安全性(墙体斜截面抗剪承载力)与经济性(墙体造价)的多目标优化设计数学模型,并采用遗传算法进行优化计算,最后给出了计算算例。理论分析与计算结果表明:将多目标优化设计方法用于密肋复合墙体的优化设计中,可使各个分目标都较为满意,兼顾了墙体安全性与经济性两方面的要求;方法合理、可行,对密肋复合墙体的设计有一定参考意义。     

Fast network multi-objective design algorithm combined with an a posteriori procedure for reliability evaluation under various operational scenarios

This paper presents a methodology for the optimal design of water supply networks. It features a multi-objective optimisation (aimed at minimising costs and maximising resilience) and a subsequent ‘retrospective’ evaluation of network reliability under various operational scenarios. The multi-objective optimisation is based on an algorithm specifically developed for the design of real networks which feature a very high number of nodes and pipes. The ‘retrospective’ evaluation of network reliability is assessed considering resilience contrasted with several other indexes adopted to describe the operational performance of the network under critical scenarios such as segment isolation or hydrant activation, and different water demand conditions. In the applications two case studies, made up of a simple benchmark network and a real network respectively, are considered for the multi-objective optimisation; the ‘retrospective’ evaluation of reliability is performed only on the real network. The latter example clearly highlights that the procedure proposed allows reliability and performance to be offset against cost, consenting informed choice of the optimal network configuration.     

A multi-objective structural optimization method for serviceability design of tall buildings

Structural optimization design aims to identify optimal design variables corresponding to a minimum objective function with constraints on performance requirements. To this end, many optimization frameworks have been proposed to determine optimal structural systems that are subjected to seismic and wind hazards in isolation. However, some modern tall buildings are sensitive to seismic and wind excitation owing to their complex structural systems and geographic regions. Therefore, a proper structural optimization method for such buildings is required to ensure that the expected performance is achieved in a multi-hazard scenario. This study proposes a multi-objective serviceability design optimization methodology for buildings in multi-hazard seismic and wind environments by combining optimality criteria and the nondominated sorting genetic algorithm II (NSGA-II). Seismic and wind effects can be instantaneously updated due to changes in the structural dynamic properties during the optimal design process. A neural-network-based surrogate model with self-updating is proposed to predict the structural natural frequency so that the overall computation time of the optimization process can be reduced. The proposed method was used to optimize a 50-story frame-tube building and was compared against the general genetic algorithm and general NSGA-II to verify the feasibility and effectiveness.     

型钢混凝土柱多目标优化设计方法研究

为探讨适用于组合结构构件的多目标优化设计方法,以型钢混凝土柱多目标优化设计为研究背景,在工程结构优化理论及组合结构设计理论的基础上,将优化目标设定为工程造价最小化和斜截面抗剪承载力最大化,采用线性加权法构造评价函数,通过调整加权系数来改变两个优化目标在优化中的重要程度,同时选择灵敏度较高的设计变量,综合考虑各种约束条件,给出了型钢混凝土柱多目标优化设计的数学模型,并借助复形法的优化思想,利用MATLAB编程求解有约束条件的非线性优化问题。优化设计实例表明,所采取的优化方法和优化思路对型钢混凝土柱优化设计是可行、有效的,可为组合结构构件的优化设计及工程应用提供参考。     

Inverse design of the thermal environment in an airplane cockpit using the adjoint method with the momentum method

Currently, the thermal environment in airplane cockpits is unsatisfactory and pilots often complain about a strong draft sensation in the cockpit. It is caused by the unreasonable air supply diffusers design. One of the best approaches to design a better cockpit environment is the adjoint method. The method can simultaneously and efficiently identify the number, size, location, and shape of air supply inlets, and the air supply parameters. However, the real air diffuser needed to design often have grilles, especially in the airplane cockpit, and the current method can only design the inlet as an opening. This study combined the adjoint method with the momentum method to directly identify the optimal air supply diffusers with grilles to create optimal thermal environment in an airplane cockpit (1) under ideal conditions and (2) with realistic constraints. Under the ideal conditions, the resulting design provides an optimal thermal environment for the cockpit, but it might not be feasible in practice. The design with realistic constraints provides acceptable thermal comfort in the cockpit, but it is not optimal. Thus, there is an engineering trade-off between design feasibility and optimization. All in all, the adjoint method with the momentum method can be effectively used to identify real air supply diffusers.     

Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis

Multi-objective optimization for designing of a small-scale distributed CCHP system has been performed. Small-scale combined cooling, heating, and power generation technologies represent a key resource to increase generation efficiency and reduce greenhouse gas emissions with respect to conventional separate production means. In the multi-objective optimization of the small-scale distributed CCHP system, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms however this objective has not been integrated with the thermoeconomic objective. The thermodynamic modeling has been implemented comprehensively while the economic analysis conducted in accordance with the total revenue requirement (TRR) method. One of the most suitable optimization techniques namely as genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. In the present work, reliability and availability are introduced in the thermoenvironomic model of the system, so that redundancy is embedded in the optimal solution. Risk analysis is used for decision-making of the final optimal solution from the obtained Pareto optimal frontier.     

A prototype whole-life value optimization tool for façade design

Façade design is a cross-disciplinary multi-objective optimization process. The major barrier to devising an optimal façade solution is the evaluation of the true values of alternative façade design options. A simple approach is to focus on a limited number of design criteria that can be evaluated through mono-disciplinary commercial software, while overlooking other cross-disciplinary design criteria. This paper describes a prototype whole-life value optimization tool for façade design, which accounts for the functional, financial, and environmental sustainability of alternative façade options. The tool adopts an integrated approach involving accurate simulation, systematic parametric analysis, and automatic design optimization. The tool is trialled on a real-world façade design project, and it successfully identified optimal façade solutions that outperform the original solutions obtained from the conventional façade design process.     

Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony(ABC)

The aim of this paper is to present a powerful simulation-based multi-objective optimization of building energy efficiency and indoor thermal comfort to obtain the optimal solutions of the comfort-energy efficient configurations of building envelope. The optimization method is developed by integrating a multi-objective artificial bee colony(MOABC) optimization algorithm implemented in MATLAB with EnergyP lus building energy simulation tool. The proposed optimization approach is applied to a single office room; and the building parameters, including the room rotation, window size, cooling and heating setpoint temperatures, glazing and wall material properties are considered as decision variables. In the present study, single-objective and multi-objective optimization analyses of the total annual building electricity consumption and the Predicted Percentage of Dissatisfied(PPD)are investigated to bring down the total energy cost as well as the thermal discomfort in four major climate regions of Iran, i.e. temperate, warm-dry, warm-humid and cold ones. In the results part,the achieved optimal solutions are presented in the form of Pareto fronts to reveal the mutual impacts of variables on the building energy consumption and the thermal discomfort. Finally, the ultimate optimum solution on the Pareto fronts are selected by TOPSIS decision-making method and the results of double-objective minimization problem are compared with the single-objective ones as well as the base design. The results of double-objective optimization problem indicate that in different climates, even though the total building electricity consumption increases a bit about2.9-11.3%, the PPD significantly decreases about 49.1-56.8%compared to the baseline model. In addition, the comparisons of single-objective and double-objective optimization approaches clearly show that multi-objective optimization methods yield more appropriate results respect to the single ones, mainly because of the lower deviation index value from the ideal solution.     

Cost–safety trade-off in unequal-area construction site layout planning

Cost and safety are two key elements when designing a good construction site layout planning (CSLP). Previous research works always considered CSLP from the aspect of reducing cost and treated SCLP as a single objective optimization problem. In the paper, CSLP was designed by a multi-objective optimization (MOO) model using modified Pareto-based ant colony optimization (ACO) algorithm, which could find a Pareto solution (trade-off layout) to fulfill the requirement of reducing cost and improve the site safety level simultaneously. Furthermore, in order to apply MOO model to solve unequal-area problem, the random grids-recognition strategy was employed in the proposed MOO model to solve the unequal-area site layout problems without increasing the computational complexities. A case study of a residential building project is used to validate the proposed MOO model and the results are very positive.     

优选管径法在给水管网优化设计中的应用

提出应用优选管径法（枚举技术）进行给水管网系统的优化设计计算，并编写ＷＤＯＣ软件系统进行实现。分析了我国目前给水管网系统设计、运行中存在的问题，并对标准优化技术没有在实际工作中广泛应用的原因进行分析，进一步介绍了优选管径法的技术处理和实现过程，最后以算例的形式对这一技术进行应用校核。结果表明，这一技术可完全应用于给水管网系统的新建、改扩建工程当中。     

A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem

Abstract: Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near‐optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet the constraints of highway design while minimizing total cost as the objective function. The model uses geographical information system (GIS) maps as an efficient and fast way to calculate right‐of‐way costs, earthwork costs, and any other spatial information and constraints that should be implemented in the design process. The efficiency of the algorithm was verified through a case study using an artificial map as the study region. Finally, we applied the algorithm to a real‐world example and the results were compared with the alignment found by traditional methods.     

Multi-objective evolutionary optimization for greywater reuse in municipal sewer systems

Sustainable design and implementation of greywater reuse (GWR) has to achieve an optimum compromise between costs and potable water demand reduction. Studies show that GWR is an efficient tool for reducing potable water demand. This study presents a multi-objective optimization model for estimating the optimal distribution of different types of GWR homes in an existing municipal sewer system. Six types of GWR homes were examined. The model constrains the momentary wastewater (WW) velocity in the sewer pipes (which is responsible for solids movement). The objective functions in the optimization model are the total WW flow at the outlet of the neighborhoods sewer system and the cost of the on-site GWR treatment system. The optimization routing was achieved by an evolutionary multi-objective optimization coupled with hydrodynamic simulations of a representative sewer system of a neighborhood located at the coast of Israel. The two non-dominated best solutions selected were the ones having either the smallest WW flow discharged at the outlet of the neighborhood sewer system or the lowest daily cost. In both solutions most of the GWR types chosen were the types resulting with the smallest water usage. This lead to only a small difference between the two best solutions, regarding the diurnal patterns of the WW flows at the outlet of the neighborhood sewer system. However, in the upstream link a substantial difference was depicted between the diurnal patterns. This difference occurred since to the upstream links only few homes, implementing the same type of GWR, discharge their WW, and in each solution a different type of GWR was implemented in these upstream homes. To the best of our knowledge this is the first multi-objective optimization model aimed at quantitatively trading off the cost of local/onsite GW spatially distributed reuse treatments, and the total amount of WW flow discharged into the municipal sewer system under unsteady flow conditions.