Toolbox for super-structured and super-structure free multi-disciplinary building spatial design optimisation

Multi-disciplinary optimisation of building spatial designs is characterised by large solution spaces. Here two approaches are introduced, one being super-structured and the other super-structure free. Both are different in nature and perform differently for large solution spaces and each requires its own representation of a building spatial design, which are also presented here. A method to combine the two approaches is proposed, because the two are prospected to supplement each other. Accordingly a toolbox is presented, which can evaluate the structural and thermal performances of a building spatial design to provide a user with the means to define optimisation procedures. A demonstration of the toolbox is given where the toolbox has been used for an elementary implementation of a simulation of co-evolutionary design processes. The optimisation approaches and the toolbox that are presented in this paper will be used in future efforts for research into- and development of optimisation methods for multi-disciplinary building spatial design optimisation.     

Physarum computing and topology optimisation

The plasmodium stage of the giant single-celled amoeboid organism Physarum polycephalum, or true slime mould, dynamically adapts its body plan in response to environmental stimuli, including nutrient location, temperature gradients, light exposure, and predatory threats. The construction and adaptation of the plasmodial transport network has been shown to be an efficient trade-off between minimising network distance and maximising connectivity. Optimisation of topology is also a desired trait in manufacturing and engineering, whereby the topology of an initially simple component is modified to minimise one or more properties (for example minimising the amount of substrate reduces the weight and material cost of the component) whilst maintaining other desired material properties within the component (for example strength under specific load points and conditions). Biologically inspired approaches to certain specific engineering challenges are well documented and successful. In this paper we explore at a more general level whether a cross-pollination can occur between Physarum computing and Topology Optimisation. Using an interdisciplinary modelling approach we explore whether the evolution of transport networks in a multi-agent model of Physarum has any similarities to networks formed by Topology Optimisation. We find that the Topology Optimisation method generates networks which are similar to the model slime mould networks. The Topology Optimisation networks correspond to higher regions of the Toussaint hierarchy of proximity graphs (i.e. more edges) whereas the model slime mould networks exhibit greater minimisation of network length. Since it is possible to adjust network connectivity in the multi-agent model via nutrient concentration we speculate that similar parametric adjustment may be possible to alter the connectivity of Topology Optimisation networks.     

Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms

This paper describes an experimental investigation into four nature-inspired population-based continuous optimisation methods: the Bees Algorithm, Evolutionary Algorithms, Particle Swarm Optimisation, and the Artificial Bee Colony algorithm. The aim of the proposed study is to understand and compare the specific capabilities of each optimisation algorithm. For each algorithm, thirty-two configurations covering different combinations of operators and learning parameters were examined. In order to evaluate the optimisation procedures, twenty-five function minimisation benchmarks were designed by the authors. The proposed set of benchmarks includes many diverse fitness landscapes, and constitutes a contribution to the systematic study of optimisation techniques and operators. The experimental results highlight the strengths and weaknesses of the algorithms and configurations tested. The existence and extent of origin and alignment search biases related to the use of different recombination operators are highlighted. The analysis of the results reveals interesting regularities that help to identify some of the crucial issues in the choice and configuration of the search algorithms.     

Spatial to kinematically determined structural transformations

To understand the building design process and to help designers involved, the idea of a research engine has been developed: In this engine cyclic transformations take place between spatial and structural building designs. With this engine, a design process can be studied closely and subjected to improvement, and designers can be supported. To develop the engine, in this paper a part of it is studied, namely the transformation from spatial to structural design, which can be divided into four sub transformations: (1) from spatial design to structural topology; (2) from structural topology to mechanical model; (3) from mechanical model to finite element model; (4) from finite element model to design recommendations. For the first sub transformation, two different techniques are presented: Spatial–Structural Transformation Rules and Element Selection. For the second sub transformation, also two techniques are presented: Element Approach and System Approach. Where possible, data models in EXPRESS and process models in IDEF0 are used. For the third and fourth sub transformation, new procedures have been developed using data models in EXPRESS. To test the data and process models for all four sub transformations, a simplified two-storey building, derived from a real six-storey apartment building, is used as case study. It can be concluded that the developed sub transformations function well, related to their application in the research engine, and that their development raises new research questions that have to be solved in the near future.     

Modelling and Optimal Design of a Carbon Fibre Reinforced Composite Automotive Roof

Structural optimisation was used to carry out the design of an all-composite roof for a late model passenger car. CAD modelling procedures were used to develop the simplified geometric model of the composite roof starting with production CAD data and to create a shell mesh. The shell element roof model was combined with a beam model of the entire body in order to carry out the full-body optimis-ation. Optimisation results yielded a roof which was 71% lighter than the steel automotive roof, which weighed 94 lb (42.7 kg). Although this paper focuses on the use of commercially available software, it demonstrates a level of automation which is not typically used in the automotive industry.     

Evolutionary algorithms in control systems engineering: a survey

Challenging optimisation problems, which elude acceptable solution via conventional methods, arise regularly in control systems engineering. Evolutionary algorithms (EAs) permit flexible representation of decision variables and performance evaluation and are robust to difficult search environments, leading to their widespread uptake in the control community. Significant applications are discussed in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control. Hybrid neural and fuzzy control schemes are also described. The important role of EAs in multiobjective optimisation is highlighted. Evolutionary advances in adaptive control and multidisciplinary design are predicted.     

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

ABSTRACT

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimisation domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimisation (PSO), Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-Adaptive Differential Evolution Algorithm, Comprehensive Learning Particle Swarm Optimisation, Backtracking Search Optimisation Algorithm, and Ideology Algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations is discussed as well. In addition, an illustrative example is also solved and every detail is provided.     

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single-objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals.     

Design of water distribution networks using accelerated momentum particle swarm optimisation technique

Optimisation of looped water distribution networks (WDNs) has been recognised as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimisation techniques. This article proposes the use of a new version of heuristic particle swarm optimisation (PSO) for solving this problem. In order to increase the convergence speed of the original PSO algorithm, some accelerated parameters are introduced to the velocity update equation. Furthermore, momentum parts are added to the PSO position updating formula to get away from trapping in local optimums. The new version of the PSO algorithm is called accelerated momentum particle swarm optimisation (AMPSO). The proposed AMPSO is then applied to solve WDN design problems. Some illustrative and comparative illustrative examples are presented to show the efficiency of the introduced AMPSO compared with some other heuristic algorithms.     

基于集成产品与过程建模的设计进程管理

本文研究虚拟原与设计过程模型的集成问题以及基于该集成模型的设计进程管理,首先提出基于虚拟原型的设计过程模型生成,然后讨论基于虚拟原型和设计过程集成建模的、面向并行工程的设计过程管理、最后重点讨论通过初步设计信息交换加快设计进程的问题。     

A methodological approach to the design of optimising control strategies for sewer systems

This study focuses on designing an optimisation based control for sewer system in a methodological way and linking it to a regulatory control. Optimisation based design is found to depend on proper choice of a model, formulation of objective function and tuning of optimisation parameters. Accordingly, two novel optimisation configurations are developed, where the optimisation either acts on the actuators or acts on the regulatory control layer. These two optimisation designs are evaluated on a sub-catchment of the sewer system in Copenhagen, and found to perform better than the existing control; a rule based expert system. On the other hand, compared with a regulatory control technique designed earlier in Mollerup et al. (2015), the optimisation showed similar performance with respect to minimising overflow volume. Hence for operation of small sewer systems, regulatory control strategies can offer promising potential and should be considered along more advanced strategies when identifying novel solutions.     

用于求解对称旅行商问题的粒子群算法和蚂蚁算法的融合

近年来,基于仿生学的随机优化技术成为学术界研究的重点问题之一,并在许多领域得到应用。粒子群优化（PSO）算法和蚂蚁算法ACO（Ant Colong Optimization）是随机全局优化的两个重要方法。PSO算法初始收敛速度较快,但在接近最优解时,收敛速度较慢,而ACO正好相反。结合二者的优势,先利用粒子群算法,再结合蚂蚁算法,以对称旅行商问题为例进行了仿真实现。实验结果表明,先利用PSO算法进行初步求解,在利用蚂蚁算法进行精细求解,可以得到较好的效果。     

CAD based shape optimization for gas turbine component design

In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. Common to both methodologies is the need to vary the object’s shape and to assess the resulting change in performance, both executed within an automatic loop. This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. This paper presents the adopted methodology and the achieved results when performing optimisation of a gas turbine disk. Our approach to hierarchical modelling employing design tables is presented, with methods to ensure satisfactory geometry variation by commercial CAD systems. The conducted studies included stochastic and probabilistic design optimisation. To solve the multi-objective optimisation problem, a Pareto optimum criterion was used. The results demonstrate that CAD centric approach enables significant progress towards automating the entire process while achieving a higher quality product with the reduced susceptibility to manufacturing imperfections.     

An automated stabilisation method for spatial to structural design transformations

A spatial–structural design process can be investigated via a so-called research engine, in which a spatial design is transformed into a structural design and vice versa. During the transformation from a spatial into a structural design, it is necessary to obtain a stable structural model, so that a structural analysis can be carried out. This article presents four methods to automate the (normally carried out intuitively) stabilisation process, using data related to a structural design’s geometry and its instability modes. The methods all use the null space and associated null vectors of the structural stiffness matrix. Then each null vector is resolved by either (a) rod addition, (b) plane addition, (c) hinge fixation by single rod substitution, or (d) hinge fixation by coupled rod substitution. The methods have been implemented in C++ and several test cases have been carried out. The test cases explain why (a) rod addition provides the most realistic solutions, (b) if several methods are used subsequently for one problem, superfluous elements are inevitable, (c) there is a serious influence on the performance for various systems of key point numbering, (d) the efficiency of the methods is not optimal and may be improved by some suggested strategies.     

An Evolutionary Parallel Tabu Search approach for distribution systems reinforcement planning

In this paper a new meta-heuristic optimisation technique is proposed. The method is based on the Parallel Tabu Search (PTS) algorithm and the application is the optimal electrical distribution systems reinforcement planning through the installation of photovoltaic plants, parallel cables, capacitor banks and transformers. The issue is a combinatorial optimisation problem; the objective function is a non-linear expression of a large number of variables. In these cases, meta-heuristics have proved to work well and one of the most efficient is the Tabu Search algorithm. For large-scale problems, parallelisation improves Tabu Search computational efficiency as well as its exploration ability. In this paper, an enhanced version of PTS, Evolutionary Parallel Tabu Search (EPTS), is proposed. It performs reproduction operators on sub-neighbourhoods directing the search towards more promising areas of the search space. The problem of distribution systems reinforcement planning has been studied in detail and the results of the application show that the EPTS outperforms the PTS and Particle Swarm Optimisation algorithms.The algorithm's performance is also tested on mathematical test functions and other properties of the proposed algorithm are examined.     

A target recognition method for maritime surveillance radars based on hybrid ensemble selection

In order to improve the generalisation ability of the maritime surveillance radar, a novel ensemble selection technique, termed Optimisation and Dynamic Selection (ODS), is proposed. During the optimisation phase, the non-dominated sorting genetic algorithm II for multi-objective optimisation is used to find the Pareto front, i.e. a set of ensembles of classifiers representing different tradeoffs between the classification error and diversity. During the dynamic selection phase, the meta-learning method is used to predict whether a candidate ensemble is competent enough to classify a query instance based on three different aspects, namely, feature space, decision space and the extent of consensus. The classification performance and time complexity of ODS are compared against nine other ensemble methods using a self-built full polarimetric high resolution range profile data-set. The experimental results clearly show the effectiveness of ODS. In addition, the influence of the selection of diversity measures is studied concurrently.     

A survey of structural and multidisciplinary continuum topology optimization: post 2000

Topology optimization is the process of determining the optimal layout of material and connectivity inside a design domain. This paper surveys topology optimization of continuum structures from the year 2000 to 2012. It focuses on new developments, improvements, and applications of finite element-based topology optimization, which include a maturation of classical methods, a broadening in the scope of the field, and the introduction of new methods for multiphysics problems. Four different types of topology optimization are reviewed: (1) density-based methods, which include the popular Solid Isotropic Material with Penalization (SIMP) technique, (2) hard-kill methods, including Evolutionary Structural Optimization (ESO), (3) boundary variation methods (level set and phase field), and (4) a new biologically inspired method based on cellular division rules. We hope that this survey will provide an update of the recent advances and novel applications of popular methods, provide exposure to lesser known, yet promising, techniques, and serve as a resource for those new to the field. The presentation of each method’s focuses on new developments and novel applications.     

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters/states cannot be provided or determined accurately. These values can be related to the geometry (wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi-Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the first case considers robust multi-objective (single-disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero-structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.     

An evaluation methodology for crowdsourced design

In recent years, the “power of the crowd” has been repeatedly demonstrated and various Internet platforms have been used to support applications of collaborative intelligence in tasks ranging from open innovation to image analysis. However, crowdsourcing applications in the fields of design research and creative innovation have been much slower to emerge. So, although there have been reports of systems and researchers using Internet crowdsourcing to carry out generative design, there are still many gaps in knowledge about the capability and limitations of the technology. Indeed the process models developed to support traditional commercial design (e.g. Pugh’s Total Design, Agile, Double-Diamond etc.) have yet to be established for Crowdsourced Design (cDesign). As a contribution to the development of such a general model this paper proposes a cDesign framework to support the creation of crowdsourced design activities. Within the cDesign framework the effective evaluation of design quality is identified as a key component that not only enables the leveraging of a large, virtual workforce’s creative activities but is also fundamental to almost all iterative optimisation processes. This paper reports an experimental investigation into two different Crowdsourced design evaluation approaches; free evaluation and ‘Crowdsourced Design Evaluation Criteria’ (cDEC). The results are benchmarked against a ‘manual’ evaluation carried out by a panel of experienced designers. The results suggest that the cDEC approach produces design rankings that correlate strongly with the judgements of an “expert panel”. The paper concludes that cDEC assessment methodology demonstrates how Crowdsourcing can be effectively used to evaluate, as well as generate, new design solutions.