DESIGNING THE INTERACTIVE EVOLUTIONARY DESIGN STATION (IEDS)

Ian Parmee

Plymouth Engineering Design Centre

University of Plymouth

Plymouth

Devon, PL4 8AA

Email: <iparmee@plymouth.ac.uk>

1.  Introduction

Although there is much evidence of the utilisation of evolutionary and
adaptive computing technologies for system optimisation there is little
recognition of their design exploration capabilities. In particular,
their appropriate integration with conceptual design processes to
support search within predefined design spaces whilst also allowing
exploration outside of initial constraint, objective and variable
parameter bounds. Close designer interaction with evolutionary computing
strategies can result in explorative processes involving off-line
processing of initial results leading to a redefinition of the design
environment. Such redefinition and further designer / evolutionary
search can result in the discovery of innovative design solutions. The
paper introduces a number of strategies that support such exploration
within the framework of an experimental interactive evolutionary design
station (IEDS).

2 Design Team / Evolutionary Computing Interaction

Though not a definitive list the following aspects must be considered if
design team / evolutionary system integration is to be successfully
achieved and efficient interactive exploratory design processes are to
be established. The ability to efficiently sample complex design spaces
described by differing model representation / simulation (e.g.
quantitative, qualitative, linguistic, crisp, etc.); the addition,
removal and / or variation of constraints, objectives and variable
parameter bounds; the rapid identification of multiple high-performance
solutions / regions of complex spaces; the development of search /
exploration systems that can capture specific design knowledge through
extensive designer interaction; the interactive, on-line processing of
information relating to multiple design criteria concerning design,
manufacturing, economic and marketing requirements and the ability to
access regions of design feasibility, to define such regions to some
extent and to identify optimal solutions within them.

This has become evident from recent research relating to the integration
of evolutionary computing with design processes \[1\]. Entirely
machine-based evolutionary conceptual design processes are not suggested
here nor are they considered currently viable. During conceptual design,
computational search and exploration techniques can be utilised to
support the engineer across a wide range of design activity and best
utility can be achieved by developing systems that enhance the inherent
capabilities of the engineer. Successful integration can result in the
development of prototype evolutionary design tools that offer
considerable potential providing powerful extensions to design team
activity by supporting rapid, extensive exploration and stimulating
innovative reasoning during conceptual design. The utilisation of
co-operative strategies that involve distributed evolutionary search
techniques can be of significant benefit. Agent technologies \[2\] can
also offer considerable utility within such environments by providing
both a state recognition and communication capability. Co-evolving
processes may describe individual elements / components of the
engineering system; aspects related to individual disciplines (ie
multi-disciplinary co-evolution); high-level aspects relating to design,
manufacture and marketing etc or differing scenarios, operational
requirements and / or design objectives. There is a requirement for
communication both between each process and with the design team which
may relate to, for instance, the degree of constraint satisfaction in
each domain or degree of convergence. An agent-based capability for
state recognition relating to the above is required with a reactive
ability to redistribute search resource; terminate search or instigate
new search processes; relax, harden, eliminate or introduce constraints
and criteria; communicate current status or seemingly relevant data to
the design team or conversely collect and distribute relevant data via
the design team interface whilst archiving relevant information to an
on-line database or extracting data required by the search processes in
order to continue.

An agent approach integrated both with machine-based and design team
activities within the structure shown in figure 1 is proposed. The
development of such overall co-evolutionary strategies supports
meaningful integration with design team practice. The designers
themselves can be regarded as external agents extracting information
from the system and processing such information off-line which supports
the dynamic re-definition of the problem domain via the re-introduction
of reformed constraint, criteria and objective functions. This can
result in rapid, concurrent, iterative improvements The design team can
rapidly assess design concepts by utilising the search and decision
support capabilities of the evolutionary design station. Off-line
discussion based upon solutions generated by the search processes
redefines the problem space whilst adding to the engineer\'s
problem-specific knowledge-base. An interactive facility that allows a
far broader search of design alternatives within an acceptable timeframe
can be achieved.

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| ### Figure 1: The Proposed Evolutionary Design Station                |
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# 3 The Design Environment

The above concepts have been applied within a preliminary design
environment relating to military airframe design in collaboration with
British Aerospace. This is a complex design domain characterised by
uncertain requirements and fuzzy objectives relating to the long
gestation periods between initial design brief and realisation of the
product. Changes in operational requirements in addition to
technological advances cause a demand for a responsive, highly flexible
strategy where design change and compromise are inherent features for
much of the design period. Design exploration leading to innovative and
creative activity must be supported. The ability to introduce rapid
change to satisfy the many operational, engineering and marketing
considerations as they themselves change is essential.

CAPS (Computer Aided Design Studies), a BAe suite of preliminary design
models, is utilised to support airframe design. The miniCAPS model, a
much abridged version of CAPS, is used here for experimentation
purposes. MiniCAPS retains major characteristics of the overall
requirements. Eight variables generate up to eleven outputs relating to
a range of objectives. The software can be configured for
multi-objective design exploration / search and optimisation or to
maximise / minimise any one of the eleven outputs.

The following components of the IEDS system shown in figure 1 are now at
an experimental prototype phase:

**i) Rule-based preferences:** A simple mathematical language that
facilitates direct preference manipulation by the engineering designer
has been established. Ranked preferences relating to operational
requirements, multi-objectives and / or multi-disciplinary constraint
can thus be introduced and altered during an evolutionary run. Extensive
experimentation on the currently stand-alone system is underway \[3\].

## ii) Multi-objective convergence: The problem area is multi-disciplinary and there is a major requirement for satisfaction of large numbers of conflicting objectives (ie \>10). Evolutionary strategies have therefore been established that support the identification of high performance regions of a Pareto frontier relating to large numbers of objectives. This approach eliminates problems associated with the generation and subsequent manipulation / search of large data sets that describe entire high dimensional Pareto frontiers. Thus the designer can efficiently search through complex multi-dimensional objective space to identify regions of high performance relating to overall requirement \[4\].

## iii) Dynamic problem decomposition: On-line techniques that, during a co-evolutionary process, identify the sensitivity of several differing objectives to individual variable parameters have been developed. This allows the identification of variables (or specific ranges of variable values) that have little effect upon individual objective satisfaction and the subsequent conversion of such variables to fixed parameters. In this manner the variable space relating to the problem can be much reduced thereby facilitating the satisfaction of conflicting multi-disciplinary objectives \[5\].

**iv) Distributed environments:** Distributed evolutionary processes
have been established with appropriate communication relating the
multi-objective satisfaction routines of item (ii). Parallel Virtual
Machine (PVM) software has been utilised to manage basic message passing
between processes.

**vi) Software Agents:** Research has just commenced relating to the
development of software agents that monitor coevolutionary processes and
recognise states relating to degree of convergence, constraint
satisfaction and multi-objective satisfaction. It is intended that such
agents will then be able to react by reallocating search resource around
the co-evolutionary processes whilst softening / hardening constraints
and objectives.

**vii) Identification of high performance regions:** COGA
(cluster-oriented genetic algorithm) strategies for the identification
of high performance design regions relating to single or
multi-objectives are well established within the PEDC. We can extract
relevant design information from such regions and present such
information to the engineer \[6\]. Current development of these
strategies allow them to be included within the information gathering
component of the EDS.

**viii) Machine-based exploration:** Research relating to improving
evolutionary exploratory capability leading to machine-based extension
of pre-defined design spaces is also well advanced. This work will
provide a basis for a more fundamental approach relating to reactive
intelligent agent interaction \[7\] that will complement and support
engineer-based redefinition of the design space.

## 3 The Interactive Evolutionary Design Station

The overall experimental system that we wish to establish is shown in
figure one. The three (or more) evolutionary processes will each be
manipulating independent simulations that may relate to individual
operational scenarios (eg long range bomber, short range fighter etc),
differing objectives or separate engineering components that together
define a system. Although direct communication may exist between the
co-evolving elements in the form of solution migration such
communication may also be monitored and controlled by software agents if
required. The overall state of the system in addition to the state of
each process will be monitored by such agents and actions will be
implemented to ensure that the system does not prematurely converge due
to an inappropriate balance between solution exploitation and design
space exploration or a gridlock situation occurring due to an inability
to satisfy conflicting constraints / objectives. The machine-based
agents also provide direct communication to the design team informing
the engineer of current status re convergence, constraint / objective
satisfaction, problem areas etc as well as information concerning
intended agent action. Conversely the engineer, via the agent interface,
can pause the evolutionary processes and download relevant information
for off-line study. Direct intervention will then be possible to
redefine the problem area.

Alternatively information passed to the design team from the
machine-based agents may promote changes to the rule-based preferences
currently under development. Information gathering processes (IGP) will
comprise of evolutionary systems such as cluster-oriented genetic
algorithms (COGAs). The dynamic problem decomposition strategy will also
reside within the IGP constantly extracting data from the co-evolving
processes and relaying information regarding scenario / objective
sensitivity to the system variables to both the machine agents and the
design team.

4 Summary

The concept of an Evolutionary Design Station to support the search,
exploration and optimisation requirements of the engineering designer
has been introduced along with components currently under development.
Further detail of such components can be found in related papers copies
of which can be obtained from Dr. I. C. Parmee
(<iparmee@plymouth.ac.uk)>. Further information regarding this and
related research can be found at:
<http://www.tech.plym.ac.uk/soc/research/edc>