14 research outputs found
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
Parametrically Homogenized Crystal Plasticity Model for Nickel-based Superalloys: Intragranular Microstructures To Polycrystalline Aggregates
The deformation mechanics of nickel-based superalloys at every length scale are influenced by a unique array of dislocation mechanisms evolving at the nanoscale. The origin of the lower scale phenomena within a single crystal is primarily due to the existence of an ordered intermetallic γ′ precipitate phase embedded within a γ solid solution matrix phase. The chemical composition and the γ−γ′ matrix-precipitate microstructure of Ni-based superalloys are highly engineered to optimize for creep, fatigue, thermal, and corrosion-resistant properties. The precise morphology and spatial configuration of the γ′ precipitates control and impede the flow of dislocations and, therefore, govern the overall viscoplastic response of the material.
This thesis develops a hierarchical multiscale crystal plasticity framework for single crystal Ni-based superalloys. An emphasis is placed on the creation of an image-based model that captures the complex γ−γ′ configuration of experimental three-dimensional microstructures. In generating such a model, important considerations emerge regarding microstructure generation, representative volume element analysis, boundary condition selection, and homogenization methodology. Each of these issues is addressed with a combination of computational tools from statistics, machine learning, optimization, and continuum mechanics.
A multiscale modeling pipeline is established in the development of a parametrically-homogenized crystal plasticity model (PHCPM). The PHCPM explicitly incorporates morphological statistics of the γ−γ′ intragranular microstructure in the crystal plasticity constitutive coefficients. This enables highly efficient and accurate image-based polycrystalline microstructural simulations. The single crystal PHCPM development process involves: (i) construction of statistically-equivalent representative volume elements, (ii) image-based modeling with dislocation-density crystal plasticity model, (iii) identification of representative aggregated microstructural parameters, (iv) selection of PHCPM framework and (v) self-consistent homogenization.
Finally, polycrystalline studies are performed throughout to demonstrate the universality of SERVE techniques for other classes of microstructures, the role of cube slip on texture at high temperatures, and the effectiveness of the PHCPM framework for higher scale simulations
Evolutionary Algorithms
Evolutionary algorithms (EAs) are population-based metaheuristics, originally
inspired by aspects of natural evolution. Modern varieties incorporate a broad
mixture of search mechanisms, and tend to blend inspiration from nature with
pragmatic engineering concerns; however, all EAs essentially operate by
maintaining a population of potential solutions and in some way artificially
'evolving' that population over time. Particularly well-known categories of EAs
include genetic algorithms (GAs), Genetic Programming (GP), and Evolution
Strategies (ES). EAs have proven very successful in practical applications,
particularly those requiring solutions to combinatorial problems. EAs are
highly flexible and can be configured to address any optimization task, without
the requirements for reformulation and/or simplification that would be needed
for other techniques. However, this flexibility goes hand in hand with a cost:
the tailoring of an EA's configuration and parameters, so as to provide robust
performance for a given class of tasks, is often a complex and time-consuming
process. This tailoring process is one of the many ongoing research areas
associated with EAs.Comment: To appear in R. Marti, P. Pardalos, and M. Resende, eds., Handbook of
Heuristics, Springe
Automated design of genetic programming of classification algorithms.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Over the past decades, there has been an increase in the use of evolutionary algorithms (EAs) for data mining and knowledge discovery in a wide range of application domains. Data classification, a real-world application problem is one of the areas EAs have been widely applied. Data classification has been extensively researched resulting in the development of a number of EA based classification algorithms. Genetic programming (GP) in particular has been shown to be one of the most effective EAs at inducing classifiers. It is widely accepted that the effectiveness of a parameterised algorithm like GP depends on its configuration. Currently, the design of GP classification algorithms is predominantly performed manually. Manual design follows an iterative trial and error approach which has been shown to be a menial, non-trivial time-consuming task that has a number of vulnerabilities. The research presented in this thesis is part of a large-scale initiative by the machine learning community to automate the design of machine learning techniques. The study investigates the hypothesis that automating the design of GP classification algorithms for data classification can still lead to the induction of effective classifiers. This research proposes using two evolutionary algorithms,namely,ageneticalgorithm(GA)andgrammaticalevolution(GE)toautomatethe design of GP classification algorithms. The proof-by-demonstration research methodology is used in the study to achieve the set out objectives. To that end two systems namely, a genetic algorithm system and a grammatical evolution system were implemented for automating the design of GP classification algorithms. The classification performance of the automated designed GP classifiers, i.e., GA designed GP classifiers and GE designed GP classifiers were compared to manually designed GP classifiers on real-world binary class and multiclass classification problems. The evaluation was performed on multiple domain problems obtained from the UCI machine learning repository and on two specific domains, cybersecurity and financial forecasting. The automated designed classifiers were found to outperform the manually designed GP classifiers on all the problems considered in this study. GP classifiers evolved by GE were found to be suitable for classifying binary classification problems while those evolved by a GA were found to be suitable for multiclass classification problems. Furthermore, the automated design time was found to be less than manual design time. Fitness landscape analysis of the design spaces searched by a GA and GE were carried out on all the class of problems considered in this study. Grammatical evolution found the search to be smoother on binary classification problems while the GA found multiclass problems to be less rugged than binary class problems
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Global supply chain optimization: a machine learning perspective to improve caterpillar's logistics operations
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the
problem of supply chain optimization. However, such techniques are often too problemspecific or too knowledge-intensive to be implemented as in-expensive, and easy-to-use computer system. The effort required to implement an optimization system for a new instance of the problem appears to be quite significant. The development process necessitates the involvement of expert personnel and the level of automation is low. The aim of this project is to develop a set of strategies capable of increasing the level of
automation when developing a new optimization system. An increased level of automation is achieved by focusing on three areas: multi-objective optimization, optimization algorithm usability, and optimization model design. A literature review highlighted the great level of interest for the problem of multiobjective optimization in the research community. However, the review emphasized a lack of standardization in the area and insufficient understanding of the relationship between multi-objective strategies and problems. Experts in the area of optimization and artificial intelligence are interested in improving the usability of the most recent
optimization algorithms. They stated the concern that the large number of variants and parameters, which characterizes such algorithms, affect their potential applicability in real-world environments. Such characteristics are seen as the root cause for the low success of the most recent optimization algorithms in industrial applications. Crucial task for the development of an optimization system is the design of the optimization model. Such task is one of the most complex in the development process, however, it is still performed mostly manually. The importance and the complexity of the task strongly suggest the development of tools to aid the design of optimization models. In order to address such challenges, first the problem of multi-objective optimization is considered and the most widely adopted techniques to solve it are identified. Such techniques are analyzed and described in details to increase the level of standardization in the area. Empirical evidences are highlighted to suggest what type of relationship exists between strategies and problem instances. Regarding the optimization algorithm, a classification method is proposed to improve its usability and computational requirement by automatically tuning one of its key parameters, the termination condition. The algorithm understands the problem complexity and automatically assigns the best termination condition to minimize runtime. The runtime of the optimization system has been reduced by more than 60%. Arguably, the usability of the algorithm has been improved as well, as one of the key configuration tasks can now be completed automatically. Finally, a system is presented to aid the definition of the optimization model through regression analysis. The purpose of the method is to gather as much knowledge about the problem as possible so that the task of the optimization model definition requires a lower user involvement. The application of the proposed algorithm is estimated that could have saved almost 1000 man-weeks to complete the project. The developed strategies have been applied to the problem of Caterpillar’s global supply chain optimization. This thesis describes also the process of developing an optimization system for Caterpillar and highlights the challenges and research opportunities identified while undertaking this work. This thesis describes the optimization model designed for Caterpillar’s supply chain and the implementation details of the Ant Colony System, the algorithm selected to optimize the supply chain. The system is now used to design the distribution plans of more than 7,000 products. The system improved Caterpillar’s marginal profit on such products by a factor of 4.6% on average.Caterpillar Inc