3 research outputs found
An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems
Experienced users often have useful knowledge and intuition in solving
real-world optimization problems. User knowledge can be formulated as
inter-variable relationships to assist an optimization algorithm in finding
good solutions faster. Such inter-variable interactions can also be
automatically learned from high-performing solutions discovered at intermediate
iterations in an optimization run - a process called innovization. These
relations, if vetted by the users, can be enforced among newly generated
solutions to steer the optimization algorithm towards practically promising
regions in the search space. Challenges arise for large-scale problems where
the number of such variable relationships may be high. This paper proposes an
interactive knowledge-based evolutionary multi-objective optimization (IK-EMO)
framework that extracts hidden variable-wise relationships as knowledge from
evolving high-performing solutions, shares them with users to receive feedback,
and applies them back to the optimization process to improve its effectiveness.
The knowledge extraction process uses a systematic and elegant graph analysis
method which scales well with number of variables. The working of the proposed
IK-EMO is demonstrated on three large-scale real-world engineering design
problems. The simplicity and elegance of the proposed knowledge extraction
process and achievement of high-performing solutions quickly indicate the power
of the proposed framework. The results presented should motivate further such
interaction-based optimization studies for their routine use in practice.Comment: 15 pages, 10 figures in main document; 6 pages, 6 figures in
supplementary documen
Manufacturing Management and Decision Support using Simulation-based Multi-Objective Optimisation
A majority of the established automotive manufacturers are under severe competitive pressure and their long term economic sustainability is threatened. In particular the transformation towards more CO2-efficient energy sources is a huge financial burden for an already investment capital intensive industry. In addition existing operations urgently need rapid improvement and even more critical is the development of highly productive, efficient and sustainable manufacturing solutions for new and updated products. Simultaneously, a number of severe drawbacks with current improvement methods for industrial production systems have been identified. In summary, variation is not considered sufficient with current analysis methods, tools used are insufficient for revealing enough knowledge to support decisions, procedures for finding optimal solutions are not considered, and information about bottlenecks is often required, but no accurate methods for the identification of bottlenecks are used in practice, because they do not normally generate any improvement actions. Current methods follow a trial-and-error pattern instead of a proactive approach. Decisions are often made directly on the basis of raw static historical data without an awareness of optimal alternatives and their effects. These issues could most likely lead to inadequate production solutions, low effectiveness, and high costs, resulting in poor competitiveness. In order to address the shortcomings of existing methods, a methodology and framework for manufacturing management decision support using simulation-based multi-objective optimisation is proposed. The framework incorporates modelling and the optimisation of production systems, costs, and sustainability. Decision support is created through the extraction of knowledge from optimised data. A novel method and algorithm for the detection of constraints and bottlenecks is proposed as part of the framework. This enables optimal improvement activities with ranking in order of importance can be sought. The new method can achieve a higher improvement rate, when applied to industrial improvement situations, compared to the well-established shifting bottleneck technique. A number of “laboratory” experiments and real-world industrial applications have been conducted in order to explore, develop, and verify the proposed framework. The identified gaps can be addressed with the proposed methodology. By using simulation-based methods, stochastic behaviour and variability is taken into account and knowledge for the creation of decision support is gathered through post-optimality analysis. Several conflicting objectives can be considered simultaneously through the application of multi-objective optimisation, while objectives related to running cost, investments and other sustainability parameters can be included through the use of the new cost and sustainability models introduced. Experiments and tests have been undertaken and have shown that the proposed framework can assist the creation of manufacturing management decision support and that such a methodology can contribute significantly to regaining profitability when applied within the automotive industry. It can be concluded that a proof-of-concept has been rigorously established for the application of the proposed framework on real-world industrial decision-making, in a manufacturing management context.Volvo Car Corporation, Sweden
University of Skövde, Swede
Multi-objective optimisation methods applied to aircraft techno-economic and environmental issues
Engineering methods that couple multi-objective optimisation (MOO) techniques
with high fidelity computational tools are expected to minimise the environmental
impact of aviation while increasing the growth, with the potential to reveal innovative
solutions. In order to mitigate the compromise between computational
efficiency and fidelity, these methods can be accelerated by harnessing the computational
efficiency of Graphic Processor Units (GPUs).
The aim of the research is to develop a family of engineering methods to support
research in aviation with respect to the environmental and economic aspects. In order
to reveal the non-dominated trade-o_, also known as Pareto Front(PF), among
conflicting objectives, a MOO algorithm, called Multi-Objective Tabu Search 2
(MOTS2), is developed, benchmarked relative to state-of-the-art methods and accelerated
by using GPUs. A prototype fluid solver based on GPU is also developed,
so as to simulate the mixing capability of a microreactor that could potentially be
used in fuel-saving technologies in aviation. By using the aforementioned methods,
optimal aircraft trajectories in terms of flight time, fuel consumption and emissions
are generated, and alternative designs of a microreactor are suggested, so as
to assess the trade-offs between pressure losses and the micro-mixing capability.
As a key contribution to knowledge, with reference to competitive optimisers
and previous cases, the capabilities of the proposed methodology are illustrated
in prototype applications of aircraft trajectory optimisation (ATO) and micromixing
optimisation with 2 and 3 objectives, under operational and geometrical
constraints, respectively. In the short-term, ATO ought to be applied to existing
aircraft. In the long-term, improving the micro-mixing capability of a microreactor
is expected to enable the use of hydrogen-based fuel. This methodology
is also benchmarked and assessed relative to state-of-the-art techniques in ATO
and micro-mixing optimisation with known and unknown trade-offs, whereas the
former could only optimise 2 objectives and the latter could not exploit the computational
efficiency of GPUs. The impact of deploying on GPUs a micro-mixing
_ow solver, which accelerates the generation of trade-off against a reference study,
and MOTS2, which illustrates the scalability potential, is assessed.
With regard to standard analytical function test cases and verification cases
in MOO, MOTS2 can handle the multi-modality of the trade-o_ of ZDT4, which
is a MOO benchmark function with many local optima that presents a challenge
for a state-of-the-art genetic algorithm for ATO, called NSGAMO, based on case
studies in the public domain. However, MOTS2 demonstrated worse performance
on ZDT3, which is a MOO benchmark function with a discontinuous trade-o_,
for which NSGAMO successfully captured the target PF. Comparing their overall
performance, if the shape of the PF is known, MOTS2 should be preferred in
problems with multi-modal trade-offs, whereas NSGAMO should be employed in discontinuous PFs. The shape of the trade-o_ between the objectives in airfoil
shape optimisation, ATO and micro-mixing optimisation was continuous. The
weakness of MOTS2 to sufficiently capture the discontinuous PF of ZDT3 was not
critical in the studied examples … [cont.]