Robust design optimisation using multi-objective evolutionary algorithms

In this paper, a new robust design method is investigated with a hierarchical asynchronous parallel multi-objective evolutionary algorithms in an optimisation framework environment to solve single and multi-point design optimisation problems in aerodynamics. The single design techniques produce solutions that perform well for the selected design point but have poor off-design performance. Here, it is shown how the approach can provide robust solutions using game theory in the sense that they are less sensitive to little changes of input parameters. Starting from a statistical definition of stability, the method captures, simultaneously Pareto non-dominated solutions with respect to performance and stability criteria, offering alternative choices to the designer.     

Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation

One of most important challenges in Unmanned (Combat) Aerial Vehicles (UCAV) is improvement of survivability and that can be achieved by well designed aerodynamic and Radar Cross Section (RCS) shapes. The aerodynamic efficiency aims to providing a short distance take-off, long endurance and better maneuverability. In addition, the stealth property is one of the essential requirements to complete diverse missions and ensure the survivability of UAVs. This paper explores the application of a robust Evolutionary Algorithm (EA) for aerofoil sections and wing plan form shape design and optimisation for the improvement of aerodynamic performance and the reduction of Radar Cross Section. The method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Results obtained from the optimisation show that utilising the designing transonic wing aerofoil sections and plan form in combination with evolutionary techniques improve the aerodynamic efficiency. It is shown that this optimisation procedure produced a set of shock-free aerofoils and achieved supercritical aero-diamond wings. Results also indicate that the method is efficient and produces optimal and Pareto non-dominated solutions.     

Robust eigenstructure clustering by non-smooth optimisation

We extend classical eigenstructure assignment to more realistic problems, where additional performance and robustness specifications arise. Our aim is to combine time-domain constraints, as reflected by pole location and eigenvector structure, with frequency-domain objectives such as the H₂, H_∞ or Hankel norms. Using pole clustering, we allow poles to move in polydisks of prescribed size around their nominal values, driven by optimisation. Eigenelements, that is poles and eigenvectors, are allowed to move simultaneously and serve as decision variables in a specialised non-smooth optimisation technique. Two aerospace applications illustrate the power of the new method.     

Robust swarm optimisation for fuzzy open shop scheduling

In this paper we consider a variant of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. Solutions to this problem are fuzzy schedules, which we argue should be seen as predictive schedules, thus establishing links with the concept of robustness and a measure thereof. We propose a particle swarm optimization (PSO) approach to minimise the schedule’s expected makespan, using priorities to represent particle position, as well as a decoding algorithm to generate schedules in a subset of possibly active ones. Our proposal is evaluated on a varied set of several benchmark problems. The experimental study includes a parametric analysis, results of the PSO compared with the state-of-the-art, and an empirical study of the robustness of taking into account uncertainty along the scheduling process.     

Robust multiobjective optimisation for fuzzy job shop problems

In this paper we tackle a variant of the job shop scheduling problem with uncertain task durations modelled as fuzzy numbers. Our goal is to simultaneously minimise the schedule's fuzzy makespan and maximise its robustness. To this end, we consider two measures of solution robustness: a predictive one, prior to the schedule execution, and an empirical one, measured at execution. To optimise both the expected makespan and the predictive robustness of the fuzzy schedule we propose a multiobjective evolutionary algorithm combined with a novel dominance-based tabu search method. The resulting hybrid algorithm is then evaluated on existing benchmark instances, showing its good behaviour and the synergy between its components. The experimental results also serve to analyse the goodness of the predictive robustness measure, in terms of its correlation with simulations of the empirical measure.     

Implementation of set-based design in multidisciplinary design optimization

Set-based design is a design approach where feasible regions for the design variables are determined from different disciplines, with the goal of locating and working with the areas of feasible overlap. During the process the constraints are adjusted in order to accommodate conflicting requirements between disciplines. The main objective of set-based design is to narrow the design space, while delaying the pursuit of a single point design as much as possible. This process avoids finalizing decisions early and allows for flexibility in dealing with requirement creep. This paper presents the development and application of a new multidisciplinary design optimization (MDO) algorithm inspired by the principles of set-based design. The new MDO algorithm was developed with the core concept of describing the design using sets to incorporate features of set-based design and achieve greater flexibility than with a single-point optimization. The MDO algorithm was applied to a ship design problem and the ship design application demonstrated the value of utilizing set-based design as a space-reducing technique before approaching the problem with a point-based optimization. Furthermore, incorporating flexibility in the constraints allowed the optimization to handle a problem with very strict constraints in a rational manner and minimize the necessary constraint violation.     

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.     

Robust incoherent control of qubit systems via switching and optimisation

A robust incoherent quantum control scheme via projective measurements plus unitary transformations is proposed for driving a qubit system from an unknown initial mixed state to an arbitrary target pure state. This scheme consists of two main steps: projective measurement on the initial mixed state and optimal control between two pure states. The first step projects the initial state into an eigenstate of the qubit system by projective measurement and guarantees that the proposed scheme is robust to different initial mixed states. The second step finds a set of suitable optimal controls to drive the qubit system from the conditional eigenstate to the target pure state. The connection between the two steps is accomplished by a switching strategy. To accomplish the second step, two approaches are presented in detail. These approaches are time-optimal transition with unbounded control and bang-bang control with minimal switches. The minimal time and minimal number of switches in these approaches can be calculated by simple analytical expressions. The proposed approaches provide two relatively straightforward optimal design methods.     

Multiparametric optimization for multidisciplinary engineering design

The area of Multiparametric Optimization (MPO) solves problems that contain unknown problem data represented by parameters. The solutions map parameter values to optimal design and objective function values. In this paper, for the first time, MPO techniques are applied to improve and advance Multidisciplinary Design Optimization (MDO) to solve engineering problems with parameters. A multiparametric subgradient algorithm is proposed and applied to two MDO methods: Analytical Target Cascading (ATC) and Network Target Coordination (NTC). Numerical results on test problems show the proposed parametric ATC and NTC methods effectively solve parametric MDO problems and provide useful insights to designers. In addition, a novel Two-Stage ATC method is proposed to solve nonparametric MDO problems. In this new approach elements of the subproblems are treated as parameters and optimal design functions are constructed for each one. When the ATC loop is engaged, steps involving the lengthy optimization of subproblems are replaced with simple function evaluations.     

Pareto-based negotiation in distributed multidisciplinary design

The process of distributed engineering design calls for a methodology making use of the most recent advances in optimization-based design including multidisciplinary and multiobjective optimization. In distributed design, the participating teams do not have access to the design problems of other teams but may exchange limited information about their own current designs, making negotiation among themselves a key mechanism to reach a desired compromise which, nevertheless, is also a Pareto design to the original problem. A mathematical model of this distributed but decomposable design process is posed and solved using Lagrangian relaxation, while Pareto optimality is equivalently converted to single-objective optimality by means of multicriteria decision making strategies. The proposed coordination algorithm allows negotiation among the teams (subproblems) by sharing only limited information that is restricted to values of optimization quantities. The proposed modeling and solution scheme is applied to a numerical example representing the design of vehicle subsystems and components.     

Computer-aided integration of multidisciplinary design information

This paper addresses the integration issues at the preliminary design stage in order to support analysis and decision-making while considering a design from the viewpoint of different disciplines. The paper describes a research project for investigating and designing a framework for intelligent linkage between design drawings and other information system environments, providing access to both external databases and design methods at the preliminary design stage. Accessing such information at this stage will allow designers to carry out the rapid evaluation of design alternatives, analysis and decision-making in a multi-disciplinary, multi-agent design environment. The objectives of the research are outlined, the methodology is discussed and the first application results are demonstrated.     

Component-oriented decomposition for multidisciplinary design optimization in building design

The potential of Multidisciplinary Design Optimization (MDO) is not sufficiently exploited in current building design practice. I argue that this field of engineering requires a special setup of the optimization model that considers the uniqueness of buildings, and allows the designer to interact with the optimization in order to assess qualities of aesthetics, expression, and building function. For this reason, the approach applies a performance optimization based on resource consumption extended by preference criteria. Furthermore, building design-specific components serve for the decomposition and an interactive way of working. The component scheme follows the Industry Foundation Classes (IFC) as a common Building Information Model (BIM) standard in order to allow a seamless integration into an interactive CAD working process in the future. A representative case study dealing with a frame-based hall design serves to illustrate these considerations. An N-Square diagram or Design Structure Matrix (DSM) represents the system of components with the disciplinary dependencies and workflow of the analysis. The application of a Multiobjective Genetic Algorithm (MOGA) leads to demonstrable results.     

Rotor design optimization using a multidisciplinary approach

A multidisciplinary optimization tool for helicopter rotor blade design has been developed. It uses a comprehensive analysis program, CAMRAD/JA, capable of performing analyses in all involved disciplines in a consistent and efficient manner, together with CONMIN's method of feasible directions. Design variables, constraints, and objective functions have been chosen to address actual design requirements in a realistic manner. The optimization procedure setup provides the flexibility to take full advantage of the comprehensive nature of the analysis code, allowing optimization driven by aerodynamic, aeroelastic, and flight mechanics design requirements. The optimization tool is applied to the McDonnell Douglas Helicopter Company AH-64A, a modern, high performance helicopter. Results are presented for combined hover/forward flight performance optimization, fuselage vibration reduction, and combined performance/vibration optimization. Blade aerodynamic and structural properties are used as design variables. The optimized designs show significant improvements and demonstrate that a practical and efficient optimization tool has been developed.Paper AIAA 91-0477, presented at the 29th Aerospace Sciences Meeting, January 7–10, 1991, Reno, Nevada     

一种多学科设计优化工作流验证方法

在多学科设计优化集成系统中,设计过程和优化求解算法均通过可视化工作流实现,工作流有效性验证对提高设计效率和提高系统的用户体验具有重要意义。当前验证方法主要针对办公自动和企业管理系统中的工作流验证问题,多学科设计优化集成系统中的工作流验证问题研究较少。在分析前期工作验证技术的基础上,针对以循环结构为特征的优化环,提出一种基于图论方法的,名为浓缩环(concentration-loop)的验证算法。结合发射平台数字化设计系统的设计与实现,对该算法进行了验证。     

Distributed control parallelism in multidisciplinary aircraft design

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g. the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.). This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance. Copyright © 1999 John Wiley & Sons, Ltd.     

An overview to structural seismic design optimisation frameworks

The application of the performance-based seismic design concept using alternative formulations is presented in this work. The formulations discussed, are implemented within an automated structural design framework using a metaheuristic optimisation algorithm. Such frameworks are able to accommodate any advanced – linear or nonlinear, static or dynamic – analysis procedure and thus replace, the conventional trial-and-error process. The formulations presented treat the seismic design problem in a deterministic or a probabilistic manner, with one or more objectives that represent the initial cost or the cost of future earthquake losses that may occur during the lifetime of a structural system. Furthermore, the implementations discussed are all consistent with the performance-based design concept and take into consideration the structural response at a number of limit-states, from serviceability to collapse.     

Quality optimisation of software architectures and design specifications

This special issue of the Journal of Systems and Software presents novel software architecture optimisation frameworks. The majority of the approaches consider the problem of optimising conflicting quality attributes simultaneously. Other approaches focus on effectively searching for better software architectures by either using smart problem-dependent heuristics or by combining the expression power of ADLs with architecture optimisation.     

A survey of multidisciplinary design optimization methods in launch vehicle design

Optimal design of launch vehicles is a complex problem which requires the use of specific techniques called Multidisciplinary Design Optimization (MDO) methods. MDO methodologies are applied in various domains and are an interesting strategy to solve such an optimization problem. This paper surveys the different MDO methods and their applications to launch vehicle design. The paper is focused on the analysis of the launch vehicle design problem and brings out the advantages and the drawbacks of the main MDO methods in this specific problem. Some characteristics such as the robustness, the calculation costs, the flexibility, the convergence speed or the implementation difficulty are considered in order to determine the methods which are the most appropriate in the launch vehicle design framework. From this analysis, several ways of improvement of the MDO methods are proposed to take into account the specificities of the launch vehicle design problem in order to improve the efficiency of the optimization process.