- author = "Ivars Dzalbs",
- title = "OptPlatform: metaheuristic optimisation framework for solving complex real-world problems",
- school = "Dept of Electronic and Computer Engineering, Brunel University",
- year = "2021",
- address = "London, UK",
- month = jan,
- keywords = "genetic algorithms, genetic programming, GPU, Intel Xeon Phi, AVX, SIMD, ant colony optimisation, ACO, Optimization, Metaheuristics, Supply chain optimisation, Automated tuning",
-
URL = "
http://bura.brunel.ac.uk/handle/2438/22848",
-
URL = "
https://bura.brunel.ac.uk/bitstream/2438/22848/1/FulltextThesis.pdf",
- size = "187 pages",
-
abstract = "We optimise daily, whether that is planning a round
trip that visits the most attractions within a given
holiday budget or just taking a train instead of
driving a car in a rush hour. Many problems, just like
these, are solved by individuals as part of our daily
schedule, and they are effortless and straightforward.
If we now scale that to many individuals with many
different schedules, like a school timetable, we get to
a point where it is just not feasible or practical to
solve by hand. In such instances, optimisation methods
are used to obtain an optimal solution. In this thesis,
a practical approach to optimisation has been taken by
developing an optimisation platform with all the
necessary tools to be used by practitioners who are not
necessarily familiar with the subject of
optimisation.
First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time.
Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs.
Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches.
Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment.",
-
notes = "a little on GP?
Supervisor: Tatiana Kalganova and Hongying Meng",
