21 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
Development of an Optimization Framework for the Design of High Speed Planing Craft
High speed planing craft play key roles in supporting several critical maritime activities, e.g., coastal surveillance, reconnaissance, life-saving operations, passenger and high value cargo transport. Despite their significant use, formal optimization frameworks have rarely been proposed to deal with their design challenges. In this thesis, an optimization framework for the preliminary design of high speed planing craft is presented. Several case studies of single- and multi-objective formulations of high speed planing craft design problem are solved using state-of-the-art optimization algorithms. The notion of scenario-based design optimization and innovization, i.e. a means to uncover design relations are also discussed.
A modular, extensible design optimization framework that allows the analysis tools to be extended or replaced with the desired level of complexity or with the state-of-the-art analysis tools is proposed in this thesis. A validated 3D mathematical model of high speed planing craft hull form has been identified in this thesis. The use of global parametric transformation that preserves surface fairness and allows for the presence of curve discontinuities is incorporated. A suite of three state-of-the-art optimization algorithms, namely NSGA-II, IDEA and SA-EA is incorporated within the framework. The performances of the algorithms are compared using the case studies. Solutions to single-objective minimization of calm water resistance, resistance in a seaway and multi-objective formulations considering minimization of total resistance, vertical impact acceleration and steady turning diameter have been presented. The capability of the framework to capture design trade-offs is illustrated. The case studies are extended to provide for scenario-based design optimization in order to demonstrate the capability of the framework to solve optimization problems based on the ship's operational profile and operating conditions.
A concept of innovization, which allows for the automatic discovery of design rules governing optimum hull forms, is introduced. The relationship gathered through the process of innovization is applied as a cheap pseudo-performance indicator within an optimization formulation, where the results compare favourably with the empirical estimate obtained from experimental data. Such extensions are new contributions to the ship design discipline, in which opens up the possibility of the development of optimum design rules for any particular ship class
Increasing the density of available pareto optimal solutions
The set of available multi-objective optimization
algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult - mainly due
to the computational cost - to use a population large enough
to ensure the likelihood of obtaining a solution close to the DMs preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimization algorithm. This method, which we refer to as Pareto estimation, is tested against a set of 2 and 3-objective test problems and a 3-objective portfolio optimization problem to illustrate its’ utility for a real-world problem
Multiobjective Design and Innovization of Robust Stormwater Management Plans
In the United States, states are federally mandated to develop watershed
management plans to mitigate pollution from increased impervious surfaces due to land development such as buildings, roadways, and parking lots. These plans require a major investment in water retention infrastructure, known as structural Best Management Practices (BMPs). However, the discovery of BMP configurations that simultaneously minimize implementation cost and pollutant load is a complex problem. While not required by law, an additional challenge is to find plans that not only meet current pollutant load targets, but also take into consideration anticipated changes in future precipitation patterns due to climate change. In this dissertation, a multi-scale, multiobjective optimization method is presented to tackle these three objectives. The method is demonstrated on the Bartlett Brook mixed-used impaired watershed in South Burlington, VT. New contributions of this work include: (A) A method for encouraging uniformity of spacing along the non-dominated front in multiobjective evolutionary optimization. This method is implemented in multiobjective differential evolution, is validated on standard benchmark biobjective problems, and is shown to outperform existing methods. (B) A procedure to use GIS data to estimate maximum feasible BMP locations and sizes in subwatersheds. (C) A multi-scale decomposition of the watershed management problem that precalculates the optimal cost BMP
configuration across the entire range of possible treatment levels within each subwatershed. This one-time pre-computation greatly reduces computation during the evolutionary optimization and enables formulation of the problem as real-valued biobjective global optimization, thus permitting use of multiobjective differential evolution. (D) Discovery of a computationally efficient surrogate for sediment load. This surrogate is validated on nine real watersheds with different characteristics and is
used in the initial stages of the evolutionary optimization to further reduce the computational burden. (E) A lexicographic approach for incorporating the third objective of finding non-dominated solutions that are also robust to climate change. (F) New visualization methods for discovering design principles from dominated solutions. These visualization methods are first demonstrated on simple truss and beam design problems and then used to provide insights into the design of complex watershed management plans. It is shown how applying these visualization methods to sensitivity
data can help one discover solutions that are robust to uncertain forcing conditions. In particular, the visualization method is applied to discover new design principles that may make watershed management plans more robust to climate change
Optimisation of opaque building envelope components with Phase Change Materials
The objective of the present thesis is to provide a methodological approach for the design of responsive building envelope components through the application of optimisation analyses. In detail, this approach was applied to opaque building envelope components with Phase Change Materials (PCMs). Since multi-objective optimisation problems generally result in a series of trade-off solutions called Pareto-front, the main focus was to investigate which values assumed by the optimisation variables led to the optimal set of solutions. In this way, the optimisation analysis was used as a tool to gain knowledge on specific problems.
After an overview on PCMs and on the application of optimisation analyses to the building envelope for improving the energy efficiency of buildings, three levels of analysis were explored; material level, component level and building level.
At the material level, the optimisation approach was applied to estimate the temperature-dependent specific heat curve of PCMs through best-fit of experimental data. Given the measured surface temperatures of a sample as boundary conditions and the known thermo-physical properties of the materials to a numerical model, the curve which minimised the difference between measured and simulated heat fluxes on both faces of the sample was found.
At the component level, “equivalent” parameters for the dynamic thermal characterisation of opaque building envelope components with PCM were proposed. Starting from the definition of the traditional dynamic thermal properties according to ISO 13786:2007, a monthly equivalent periodic thermal transmittance and the corresponding time shift were defined by imposing steady-periodic conditions with monthly average external air temperature and solar irradiance profiles while keeping a constant air temperature on the internal side. Then, the monthly equivalent values were synthesised in a unique yearly value by means of a simple average. A parametric model was subsequently developed to describe PCM-enhanced multi-layer walls with simultaneous use of at most two PCMs, and an optimisation analysis was carried out for three locations (Palermo, Torino and Oslo) to find wall layout and PCMs' thermo-physical properties (melting temperature, melting temperature range, latent heat of fusion and thermal conductivity) which minimise yearly equivalent periodic thermal transmittance, overall PCM thickness and thickness of the wall.
At the building level, the investigations focused on the application of optimisation analyses for the energy retrofit of office buildings. Three retrofit options on the opaque envelope components were considered in the aforementioned locations; intervention either on the external side of the wall, on the internal side of the wall, or on both sides of the wall. Moreover, either the same retrofit solution for all the walls or a different wall solution for each orientation were considered. In both cases, a maximum of two PCM materials could be selected by the optimisation algorithm. With regard to the objective functions, the problem was faced under two points of view. On one side, optimisations were run with three objectives to minimise the building energy need for heating, cooling and the investment cost. On the other side, the optimisations were performed with two objectives to minimise primary energy consumption and global cost. Only for the climate of Oslo, where heating is mostly electric and no cooling system was adopted, the minimisation objectives were primary energy consumption, global cost and thermal discomfort.
Even though a proper optimisation of the thermo-physical properties of PCMs was found to be especially advisable when the operation of the HVAC system implies a non-trivial solution, the results of these analyses allowed to propose a few design guidelines for PCM selection and application. However, for the analysed case studies, PCM prices need to be reduced in order to become a cost-effective retrofit option
Multi-objective mixed-integer evolutionary algorithms for building spatial design
Multi-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives. Within this field there are a number of key challenges. Among others, this includes constraint handling and the exploration of mixed-integer search spaces. This thesis investigates how these challenges can be handled at the same time, and in particular how they can be applied in the multi-objective optimisation algorithms. These algorithms are developed in the context of the optimisation of building spatial designs, which describe the exterior shape of a building, and the internal division into different spaces. Spatial designs are developed early in the design process, and thus have a large impact on the final building design, and in turn also on the quality of the building. Here the structural and thermal performance of a building are optimised to reduce resource consumption. The main contributions of this thesis are as follows. Firstly, a representation for building spatial designs in is introduced. Secondly, specialised search operators are designed to ensure only feasible solutions will be explored. Thirdly, data about the discovered solutions is analysed to explain the results to domain experts. Finally, a general purpose multi-objective mixed-integer evolutionary algorithm is developed. This work is part of the TTW-Open Technology Programme with project number 13596, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO).Computer Science
How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance
With modern requirements, there is an increasing tendency of considering
multiple objectives/criteria simultaneously in many Software Engineering (SE)
scenarios. Such a multi-objective optimization scenario comes with an important
issue -- how to evaluate the outcome of optimization algorithms, which
typically is a set of incomparable solutions (i.e., being Pareto non-dominated
to each other). This issue can be challenging for the SE community,
particularly for practitioners of Search-Based SE (SBSE). On one hand,
multi-objective optimization could still be relatively new to SE/SBSE
researchers, who may not be able to identify the right evaluation methods for
their problems. On the other hand, simply following the evaluation methods for
general multi-objective optimization problems may not be appropriate for
specific SE problems, especially when the problem nature or decision maker's
preferences are explicitly/implicitly available. This has been well echoed in
the literature by various inappropriate/inadequate selection and
inaccurate/misleading use of evaluation methods. In this paper, we first carry
out a systematic and critical review of quality evaluation for multi-objective
optimization in SBSE. We survey 717 papers published between 2009 and 2019 from
36 venues in seven repositories, and select 95 prominent studies, through which
we identify five important but overlooked issues in the area. We then conduct
an in-depth analysis of quality evaluation indicators/methods and general
situations in SBSE, which, together with the identified issues, enables us to
codify a methodological guidance for selecting and using evaluation methods in
different SBSE scenarios.Comment: This paper has been accepted by IEEE Transactions on Software
Engineering, available as full OA:
https://ieeexplore.ieee.org/document/925218