Automatic concept model generation for optimisation and robust design of passenger cars

A fully automated method of structural optimisation for the body in white structure is presented. The body in white is a technical term for the car body without windows and closures. The iterations in the optimisation loop comprise the following steps: fully parameterised design creation, automated meshing and model assembly, parallel computation and evaluation. For this purpose several free and commercially available software applications were combined, including: SFE concept, Hypermesh, Perl, Matlab, and Radioss. The optimisation was conducted using Genetic Algorithms (GA), which are ideally suited to solve problems with solution spaces that are too large to be exhaustively searched. The viability of the method is demonstrated for a vehicle component model of a front bumper system utilizing both material and geometry related properties as design variables.     

Computational algorithms to evaluate design solutions using Space Syntax

In the past, conventional computer-aided architectural design (CAAD) systems could not manage semantic information on building components and spaces but only graphical and geometric information. However, since the advent of Building Information Modeling (BIM), which has been used for managing semantic building information, determining spatial relationships as well as quantities and properties of building components in CAAD systems has become easier. It is necessary to make current CAAD systems capable of performing spatial analysis functions using BIM because they can easily recognize building components and spaces. Accordingly, this study aims to develop the computational algorithms to evaluate design solutions using Space Syntax during the process of computer-aided architectural designing. To extract topological information from design solutions, this study proposes algorithms to recognize building information produced in the form of Industry Foundation Classes (IFC), deduce the necessary topological information, and store the information in the form of matrices. The Space Syntax theory is employed to evaluate the solutions based on social properties of spaces in a building and examine the potential for adding a spatial analysis function into CAAD applications. The developed algorithms calculate the integration value for each space from spatial connectivity based on J-graphs. To validate the proposed algorithms, a program named J-Studio for Architectural Planning (J-SAP) was developed to evaluate design solutions easily and quickly. The validation results are as follows: (1) the topological information extracted from building information was decoded into a dimensionless representation and legible J-graph, (2) mathematical analyses for choosing a better design solution during computer-aided architectural designing were presented, and (3) the examination of the privacy level of each space in a building through Space Syntax analysis was discussed. Thus, this study demonstrates the possibility of determining the social properties and accessibility of spaces during the process of computer-aided architectural designing to meet client requirements by extracting topological information from building information model and performing Space Syntax analysis for evaluating alternatives using the information.     

Constrained,mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.     

A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping

Numerous real-world problems relating to ship design and shipping are characterised by combinatorially explosive alternatives as well as multiple conflicting objectives and are denoted as multi-objective combinatorial optimisation (MOCO) problems. The main problem is that the solution space is very large and therefore the set of feasible solutions cannot be enumerated one by one. Current approaches to solve these problems are multi-objective metaheuristics techniques, which fall in two categories: population-based search and trajectory-based search. This paper gives an overall view for the MOCO problems in ship design and shipping where considerable emphasis is put on evolutionary computation and the evaluation of trade-off solutions. A two-stage hybrid approach is proposed for solving a particular MOCO problem in ship design, subdivision arrangement of a ROPAX vessel. In the first stage, a multi-objective genetic algorithm method is employed to approximate the set of pareto-optimal solutions through an evolutionary optimisation process. In the subsequent stage, a higher-level decision-making approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker.     

Multidisciplinary design optimisation and robust design approaches applied to concurrent design

The increasing economic competition of all industrial markets and growing complexity of engineering problems lead to a progressive specialisation and distribution of expertise, tools and work sites. Most industrial sectors manage this fragmentation using the concurrent engineering approach, which is based on tools integration and shared databases and requires significant investments in design and work organisation. Besides, the multidisciplinary design optimisation (MDO) is more and more used as a method for optimal solutions search with regard to multiple coupled disciplines. The paper describes a quite innovative multidisciplinary optimisation method based on robust design techniques: MORDACE (multidisciplinary optimisation and robust design approaches applied to concurrent engineering). Managing uncertainty due to design teams collaboration, our automatic optimisation strategy allows concurrently designing different aspects or parts of a complex product. The method assures effective design work distribution and high optimisation results, containing the CPU time. In addition, our strategy is suited to the early stages of the design cycle, where evolutions of design goals and constraints are possible and exhaustive information about the design space is necessary. A roll stabiliser fin optimisation is presented as an example of this method applied to an industrial design problem.     

Gradient based hyper-parameter optimisation for well conditioned kriging metamodels

In this work a two step approach to efficiently carrying out hyper parameter optimisation, required for building kriging and gradient enhanced kriging metamodels, is presented. The suggested approach makes use of an initial line search along the hyper-diagonal of the design space in order to find a suitable starting point for a subsequent gradient based optimisation algorithm. During the optimisation an upper bound constraint is imposed on the condition number of the correlation matrix in order to keep it from being ill conditioned. Partial derivatives of both the condensed log likelihood function and the condition number are obtained using the adjoint method, the latter has been derived in this work. The approach is tested on a number of analytical examples and comparisons are made to other optimisation approaches. Finally the approach is used to construct metamodels for a finite element model of an aircraft wing box comprising of 126 thickness design variables and is then compared with a sub-set of the other optimisation approaches.     

Joint optimisation of price,warranty and recovery planning in remanufacturing of used products under linear and non-linear demand,return and cost functions

We investigate joint optimisation of remanufacturing, pricing and warranty decision-making for end-of-life products. A novel mathematical–statistical model is proposed where decisions involve pricing of returned used products (cores), degree of their remanufacturing, selling price and the warranty period for the final remanufactured products. The virtual age reliability improvement approach is chosen to model the upgrading of the cores to higher quality levels. We consider price- and warranty-dependent demand, price- and age-dependent return, and age-dependent remanufacturing cost in the model development. Both linear and non-linear forms of these functions are investigated. First, under some restrictive conditions of upgrade level and age distribution of received cores, special cases of the problem, which can be solved using a recently developed non-linear optimisation solver, are presented. We also implement a particle swarm optimisation algorithm for the solution of the original problem when all the restrictive assumptions are dropped. Finally, numerical experiments and sensitivity analysis are presented to address different aspects of the model and the solution approaches.     

Prioritised objectives for model predictive control of building heating systems

Advantages of Model Predictive Control (MPC) strategies for control of building energy systems have been widely reported. A key requirement for successful realisation of such approaches is that strategies are formulated in such a way as to be easily adapted to fit a wide range of buildings with little commissioning effort. This paper introduces an MPC-based building heating strategy, whereby the (typically competing) objectives of energy and thermal comfort are optimised in a prioritised manner. The need for balancing weights in an objective function is eliminated, simplifying the design of the strategy. The problem is further divided into supply and demand problems, separating a high order linear optimisation from a low order nonlinear optimisation. The performance of the formulation is demonstrated in a simulation platform, which is trained to replicate the thermal dynamics of a real building using data taken from the building.     

Digital filter design using multiple pareto fronts

Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. One such measure is clustering. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The population is split into a number of clusters, and parent and offspring selection, as well as fitness calculation, are performed on a per-cluster basis. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. They demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.Part of the material presented in this paper was published in Third NASA Workshop on Evolvable Hardware (EH 2001), 12–14 July 2001, Long Beach, California, pp. 136–145This research is generously supported by a grant from Marconi, plc. The leadership and support of John Evans is gratefully acknowledged.     

A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.     

CAD-based shape optimisation using adjoint sensitivities

Existing approaches to CAD-based design optimisation using adjoint sensitivities are reviewed and their shortcomings are recalled. An alternative approach is presented which uses the control points of the boundary representation (BRep) as design parameters. The sensitivity of the objective function with respect to the design variables is calculated using automatic differentiation (AD). Results for a 2-D aerofoil are presented.     

A new effective operator for the hybrid algorithm for solving global optimisation problems

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.     

Multi-objective ACO algorithms to minimise the makespan and the total rejection cost on BPMs with arbitrary job weights

In this paper, we investigate the batch-scheduling problem with rejection on parallel machines with non-identical job sizes and arbitrary job-rejected weights. If a job is rejected, the corresponding penalty has to be paid. Our objective is to minimise the makespan of the processed jobs and the total rejection cost of the rejected jobs. Based on the selected multi-objective optimisation approaches, two problems, P1 and P2, are considered. In P1, the two objectives are linearly combined into one single objective. In P2, the two objectives are simultaneously minimised and the Pareto non-dominated solution set is to be found. Based on the ant colony optimisation (ACO), two algorithms, called LACO and PACO, are proposed to address the two problems, respectively. Two different objective-oriented pheromone matrices and heuristic information are designed. Additionally, a local optimisation algorithm is adopted to improve the solution quality. Finally, simulated experiments are conducted, and the comparative results verify the effectiveness and efficiency of the proposed algorithms, especially on large-scale instances.     

Application of firefly algorithm and ANFIS for optimisation of functionally graded beams

Volume fraction optimisation of functionally graded beams is studied for maximising the fundamental natural frequency by applying a new meta-heuristic nature-inspired algorithm called firefly algorithm (FA) which is based on the flashing behaviour of fireflies. Nature-inspired algorithms are among the most powerful algorithms for optimisation of engineering problems. The primary optimisation variables are the three parameters in the power-law distribution. Since the search space is large, the optimisation processes becomes so complicated and too much time consuming. Thus, a suitable Adaptive Neuro-Fuzzy Inference System (ANFIS) that is based on Takagi–Sugeno fuzzy inference system is combined with FA to reproduce the behaviour of the structure in free vibration. The ANFIS improves the speed of optimisation process by a considerable amount. The results are compared with those obtained by imperialist competitive algorithm, genetic algorithm and Artificial Neural Networks proposed in our previous work. Results show that the combination of FA and ANFIS is capable of yielding better optimal solution in comparison with other available techniques. It is believed that new results are of interest to the scientific and engineering community in the area of engineering design.     

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

This paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation.A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle swarm optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.     

The generation of hierarchic structures via robust 3D topology optimisation

Commonly used building structures often show a hierarchic layout of structural elements. It can be questioned whether such a layout originates from practical considerations, e.g. related to its construction, or that it is (relatively) optimal from a structural point of view. This paper investigates this question by using topology optimisation in an attempt to generate hierarchical structures. As an arbitrarily standard design case, the principle of a traditional timber floor that spans in one direction is used. The optimisation problem is first solved using classical sensitivity and density filtering. This leads indeed to solutions with a hierarchic layout, but they are practically unusable as the floor boarding is absent. A Heaviside projection is therefore considered next, but this does not solve the problem. Finally, a robust approach is followed, and this does result in a design similar to floor boarding supported by timber joists. The robust approach is then followed to study a floor with an opening, two floors that span in two directions, and an eight-level concrete building. It can be concluded that a hierarchic layout of structural elements likely originates from being optimal from a structural point of view. Also clear is that this conclusion cannot be obtained by means of standard topology optimisation based on sensitivity or density filtering (as often found in commercial finite element codes); robust 3D optimisation is required to obtain a usable, constructible (or in the future: 3D printable) structural design, with a crisp black-and-white density distribution.     

Differential evolution and differential ant-stigmergy on dynamic optimisation problems

Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm (jDE) and the differential ant-stigmergy algorithm (DASA). The performances of the jDE and the DASA are evaluated on the set of well-known benchmark problems provided for the special session on Evolutionary Computation in Dynamic and Uncertain Environments. We analyse the results for five algorithms presented by using the non-parametric statistical test procedure. The two proposed algorithms show a consistently superior performance over other recently proposed methods. The results show that both algorithms are appropriate candidates for DOPs.     

Topology/shape optimisation of axisymmetric continuum structures – a metamorphic development approach

A novel topology/shape optimisation method for axisymmetric elastic solids, based on solid modeling and FE analysis, is presented. Optimal profiles of minimum-mass axisymmetric structures are sought by growing and degenerating simple initial structures subject to response constraints. The rates of the growth and degeneration are controlled based on the current objective and constraint functions of the optimisation problem under consideration. The optimal structures are developed metamorphically in specified infinite design domains using both quadrilateral and triangular axisymmetric finite elements that are ideally suited for modeling continua involving curved boundaries.The robustness of this fully automatic method is studied and validated with the first example of seeking the optimal shape of a centrally suspended axisymmetric object with minimum strain energy caused by self-weight. Then the method is applied to a practical industrial design problem: the design of a turbine disk. The variations of load and boundary conditions caused by shape change in these problems, including the gravitational and centrifugal loads, and temperature distribution are accommodated in the optimisation procedures. Thus, the design model closely resembles the real design problem. The results demonstrate the success of the method in generating optimal but realistic solutions to practical design problems.