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Type: Thesis
Title: Differential Evolution for Dynamic Constrained Continuous Optimisation
Author: Hasani Shoreh, Maryam
Issue Date: 2020
School/Discipline: School of Computer Science
Abstract: In this thesis, we choose the evolutionary dynamic optimisation methodology to tackle dynamic constrained problems. Dynamic constrained problems represent a common class of optimisation that occur in many real-world scenarios. Evolutionary algorithms are often considered very general search heuristics. Their main advantages (in comparison to problem-specific search methods) are their robustness, flexibility and extensibility, as well as the fact that almost no domain knowledge is required for their implementation and application. Our research is focused on the following areas. In the first part of the thesis, we modify common constraint handling techniques from static domains to suit dynamic environments. We investigate the deficiencies of such techniques and the potential of each method based on the change characteristics of the environment. In the second part, we propose a framework to create benchmarks, since we have observed a lack of benchmarks to evaluate algorithms in dynamic continuous optimisation. Third, we carry out an exhaustive empirical study of diversity mechanisms applied to solve dynamic constrained optimisation problems. Finally, we investigate the integration of a neural network into the evolution process and analyse it’s effectiveness compared to that of popular diversity mechanisms. We address the possibility of integrating such mechanisms with a neural network approach in order to improve the results.
Advisor: Neuman, Frank
Yaneli Ameca Alducin, Maria
Gao, Wanru
Dissertation Note: Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2020
Keywords: Dynamic Constrained Problems
Evolutionary Algorithm
Continuous Optimisation
Differential Evolution
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
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