23 research outputs found
Biomorpher: interactive evolution for parametric design
Combining graph-based parametric design with metaheuristic solvers has to date focussed solely on performance based criteria and solving clearly defined objectives. In this paper, we outline a new method for combining a parametric modelling environment with an interactive Cluster-Orientated Genetic Algorithm (COGA). In addition to performance criteria, evolutionary design exploration can be guided through choice alone, with user motivation that cannot be easily defined. As well as numeric parameters forming a genotype, the evolution of whole parametric definitions is discussed through the use of genetic programming. Visualisation techniques that enable mixing small populations for interactive evolution with large populations for performance-based optimisation are discussed, with examples from both academia and industry showing a wide range of applications
Evolutionary Decomposition of Complex Design Spaces
This dissertation investigates the support of conceptual engineering design through the
decomposition of multi-dimensional search spaces into regions of high performance. Such
decomposition helps the designer identify optimal design directions by the elimination of
infeasible or undesirable regions within the search space. Moreover, high levels of
interaction between the designer and the model increases overall domain knowledge and
significantly reduces uncertainty relating to the design task at hand.
The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm
(COGA) which achieves search space decomposition by using variable mutation
(vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of
high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose
design domains of unknown nature within a real-time environment, the elimination of
apriori knowledge, speed and robustness are paramount. Furthermore COGA should
promote the in-depth exploration of the entire search space, sampling all optima and the
surrounding areas. Finally any proposed system should allow for trouble free integration
within a Graphical User Interface environment.
The replacement of the variable mutation strategy with a number of algorithms which
increase search space sampling are investigated. Utility is then increased by incorporating
a control mechanism that maintains optimal performance by adapting each algorithm
throughout search by means of a feedback measure based upon population convergence.
Robustness is greatly improved by modifying the Adaptive Filter through the introduction
of a process that ensures more accurate modelling of the evolving population.
The performance of each prospective algorithm is assessed upon a suite of two-dimensional
test functions using a set of novel performance metrics. A six dimensional
test function is also developed where the areas of high performance are explicitly known,
thus allowing for evaluation under conditions of increased dimensionality. Further
complexity is introduced by two real world models described by both continuous and
discrete parameters. These relate to the design of conceptual airframes and cooling hole
geometries within a gas turbine.
Results are promising and indicate significant improvement over the vmCOGA in terms of
all desired criteria. This further supports the utilisation of COGA as a decision support
tool during the conceptual phase of design.British Aerospace plc, Warton and
Rolls Royce plc, Filto
Improving problem definition through interactive evolutionary computation
Poor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human-computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established
Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems
This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book
An Interactive Visualisation System for Engineering Design using Evolutionary Computing
This thesis describes a system designed to promote collaboration between the human and computer
during engineering design tasks. Evolutionary algorithms (in particular the genetic algorithm) can
find good solutions to engineering design problems in a small number of iterations, but a review of
the interactive evolutionary computing literature reveals that users would benefit from
understanding the design space and having the freedom to direct the search. The main objective of
this research is to fulfil a dual requirement: the computer should generate data and analyse the
design space to identify high performing regions in terms of the quality and robustness of solutions,
while at the same time the user should be allowed to interact with the data and use their experience
and the information provided to guide the search inside and outside regions already found.
To achieve these goals a flexible user interface was developed that links and clarifies the
research fields of evolutionary computing, interactive engineering design and multivariate
visualisation. A number of accessible visualisation techniques were incorporated into the system.
An innovative algorithm based on univariate kernel density estimation is introduced that quickly
identifies the relevant clusters in the data from the point of view of the original design variables or
a natural coordinate system such as the principal or independent components. The robustness of
solutions inside a region can be investigated by novel use of 'negative' genetic algorithm search to
find the worst case scenario. New high performance regions can be discovered in further runs of
the evolutionary algorithm; penalty functions are used to avoid previously found regions. The
clustering procedure was also successfully applied to multiobjective problems and used to force the
genetic algorithm to find desired solutions in the trade-off between objectives.
The system was evaluated by a small number of users who were asked to solve simulated
engineering design scenarios by finding and comparing robust regions in artificial test functions.
Empirical comparison with benchmark algorithms was inconclusive but it was shown that even a
devoted hybrid algorithm needs help to solve a design task. A critical analysis of the feedback and
results suggested modifications to the clustering algorithm and a more practical way to evaluate the
robustness of solutions. The system was also shown to experienced engineers working on their real
world problems, new solutions were found in pertinent regions of objective space; links to the
artefact aided comparison of results. It was confirmed that in practice a lot of design knowledge is
encoded into design problems but experienced engineers use subjective knowledge of the problem
to make decisions and evaluate the robustness of solutions. So the full potential of the system was
seen in its ability to support decision making by supplying a diverse range of alternative design
options, thereby enabling knowledge discovery in a wide-ranging number of applications
Exploring and Exploiting Models of the Fitness Landscape: a Case Against Evolutionary Optimization
In recent years, the theories of natural selection and biological evolution have proved
popular metaphors for understanding and solving optimization problems in engineering
design. This thesis identifies some fundamental problems associated with this use of
such metaphors. Key objections are the failure of evolutionary optimization techniques
to represent explicitly the goal of the optimization process, and poor use of knowledge
developed during the process. It is also suggested that convergent behaviour of an
optimization algorithm is an undesirable quality if the algorithm is to be applied to
multimodal problems.
An alternative approach to optimization is suggested, based on the explicit use of
knowledge and/or assumptions about the nature of the optimization problem to construct
Bayesian probabilistic models of the surface being optimized and the goal of
the optimization. Distinct exploratory and exploitative strategies are identified for
carrying out optimization based on such models—exploration based on attempting to
reduce maximally an entropy-based measure of the total uncertainty concerning the
satisfaction of the optimization goal over the space, exploitation based on evalutation
of the point judged most likely to achieve the goal—together with a composite strategy
which combines exploration and exploitation in a principled manner. The behaviour
of these strategies is empirically investigated on a number of test problems.
Results suggest that the approach taken may well provide effective optimization in
a way which addresses the criticisms made of the evolutionary metaphor, subject to
issues of the computational cost of the approach being satisfactorily addressed
Evolutionary multi-objective decision support systems for conceptual design
Merged with duplicate record 10026.1/2328 on 07.20.2017 by CS (TIS)In this thesis the problem of conceptual engineering design and the possible use of adaptive search
techniques and other machine based methods therein are explored. For the multi-objective optimisation
(MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are
used and various techniques explored: weighted sums, lexicographic order, Pareto method with and
without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e
optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is
introduced and applied to a real-world optimisation problem.
Decision support methods within conceptual engineering design framework are discussed and a new
preference method developed. The preference method for translating vague qualitative categories
(such as "more important 91
,
4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based
on fuzzy preferences and graph theory methods. Several applications of preferences are presented
and discussed:
* in weighted sum based optimisation methods;
s in weighted Pareto method;
* for ordering and manipulating constraints and scenarios;
e for a co-evolutionary, distributive GA-based MOO method;
The issue of complexity and sensitivity is addressed as well as potential generalisations of presented
preference methods. Interactive dynamical constraints in the form of design scenarios are introduced.
These are based on a propositional logic and a fairly rich mathematical language. They can be added,
deleted and modified on-line during the design session without need for recompiling the code.
The use of machine-based agents in conceptual design process is investigated. They are classified
into several different categories (e. g. interface agents, search agents, information agents). Several
different categories of agents performing various specialised task are developed (mostly dealing with
preferences, but also some filtering ones). They are integrated with the conceptual engineering design
system to form a closed loop system that includes both computer and designer.
All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering
Design Centre / British Aerospace conceptual airframe design project.British Aerospace Systems, Warto
ADAPTIVE SEARCH AND THE PRELIMINARY DESIGN OF GAS TURBINE BLADE COOLING SYSTEMS
This research concerns the integration of Adaptive Search (AS) technique such as the
Genetic Algorithms (GA) with knowledge based software to develop a research prototype
of an Adaptive Search Manager (ASM). The developed approach allows to utilise both
quantitative and qualitative information in engineering design decision making. A Fuzzy
Expert System manipulates AS software within the design environment concerning the
preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry
models have been developed for the project in collaboration with Rolls Royce plc. The
research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection
(ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design
solutions as potential design options. ARTS is a GA technique that is particularly suitable
for real world problems having multiple sub-optima. KBHC uses information gathered
during the ARTS search as well as information from the designer to perform a deterministic
hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design
solution sensitivity, design variable sensitivities and constraint sensitivities are calculated
following Taguchi's methodology, which extracts sensitivity information with a very small
number of model evaluations. Each potential design option is then qualitatively evaluated
separately for manufacturability, choice of materials and some designer's special preferences
using the knowledge of domain experts. In order to guarantee that the qualitative evaluation
module can evaluate any design solution from the entire design space with a reasonably
small number of rules, a novel knowledge representation technique is developed. The
knowledge is first separated in three categories: inter-variable knowledge, intra-variable
knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then
integrated using a concept of compromise. Information about the "good" design solutions is
presented to the designer through a designer's interface for decision support.Rolls Royce plc., Bristol (UK