Dynamic Multi-objective Optimisation Using Evolutionary Algorithms
University of Birmingham - School of Computer Science
|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||£14,296 per annum|
|Placed on:||22nd November 2016|
|Closes:||31st January 2017|
The University of Birmingham and the University of Melbourne are offering this joint PhD co-funded by both institutions providing the opportunity to study alongside world-leading academics in Birmingham and Melbourne.
Evolutionary Algorithms (EAs) have been applied successfully to a wide range of stationary optimisation problems, e.g., car engine design problems  and railway rescheduling problems . However, many real-world problems possess time-variant attributes that require frequent adaptation of optimised solutions. These dynamic attributes pose new research challenges.
In this project, we will focus on the design and analysis of novel EAs for such dynamic optimisation problems (DOPs). Unlike some of the existing work in this topic area, we will study multi-objective optimisation in dynamic and uncertain environments, including dynamics in the decision space, the objective space, constraints, and the number of objectives and decision variables.
In this project, we will first construct (discrete and continuous) dynamic test environments based on the concept of problem difficulty, scalability, cyclicity and noise of environments, and standardised performance measures for evaluating EAs for multi-objective optimisation. Based on the dynamic test and evaluation environment, we will then design and evaluate novel EAs based on our previous research . In order to better understand the fundamental issues, theoretical analysis of EAs will be pursued. We plan to apply drift analysis  to analyse the computational time complexity of EAs for DOPs.
We aim at developing a generic framework of EAs for DOPs by extracting key techniques/properties of efficient EAs for DOPs and studying the relationship between them and the characteristics of DOPs being solved with respect to the environmental dynamics in the genotypic space. While similar research might have been considered in the case of single objective dynamic optimisation, the case of multi-objective optimisation  will be a completely new topic.
The entry requirements for the Birmingham/Melbourne Joint PhD are either:
- An upper second-class four-year honours undergraduate degree in a relevant subject
- An MSc/MRes in a relevant subject
Applications are made online at the University of Birmingham website. Click on the 'Apply' button below or on the relevant University course finder page and you will be taken to the University of Birmingham Postgraduate application system. Within the application, at the Programmes open for Admission page, please select ‘EPS/University of Melbourne Joint PhD 3.5 years’. Please detail the supervisor and project title under the Research Information section of the application form. Applications should include a statement of research interests. Applicants are encouraged to contact prospective supervisors informally to discuss the project.
Additional Funding Information
A fully-funded studentship, which includes tax-free Doctoral Stipend of £14,296* per annum, is available for Home/EU and Overseas students on this Joint PhD programme between the University of Birmingham and the University of Melbourne for October 2017 start. For engineering students who are to be hosted by the University of Melbourne, the scholarship rate will be $AUD26,388 p.a. and will include provision for a return trip to Birmingham.
*subject to inflationary variation
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Midlands of England