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Study information

Multi-Objective Optimisation and Decision Making - 2024 entry

MODULE TITLEMulti-Objective Optimisation and Decision Making CREDIT VALUE15
MODULE CODECOMM510 MODULE CONVENERDr Tinkle Chugh (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

Throughout industry and science, optimisation tasks require the trading-off of multiple quality criteria which are in competition with one another. This multi-objective optimisation task often requires the search and return of a set of solutions rather than a single design. This module spans specialised ‘expensive’ optimisation approaches where the cost function can only be queries a few hundred times at most, through to those designed for real-time optimisation of multi-objective optimisation problems which change over time, and robust optimisation. It also covers multi-criterion decision making – which is concerned with how a final design is selected from a trade-off set. 

This module is appropriate for you if you have a background in a numerate subject and some experience of programming in a recognised modern computer language.

For MSc students who will not have taken ECM2423, it is recommended to take ECMM409 (but it is not a requirement).

AIMS - intentions of the module

The aim of this module is to give you a theoretical and practical understanding of the optimisation of black-box multi-objective problems. By the end of this module you should be able to recognise the different sub-tasks and problems within a multi-objective optimisation task, and reason through the appropriate selection of an optimiser. You should also be able to directly undertake decision making process for solution selection, or guide a problem owner through this. You should be able to use different multi-objective optimisation algorithms implemented in different libraries.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

Module Specific Skills and Knowledge
1 Demonstrate a clear understanding of the main categories of multi-objective optimisation;
2 Demonstrate a clear understanding of a range of multi-objective decision-making processes;
3 Implement a multi-objective optimisation software pipeline, and evaluate its performance;


Discipline Specific Skills and Knowledge:
4 Demonstrate familiarity with the main trends in multi-objective optimisation research;
5 Choose and use an appropriate development process
6 Implement software for addressing real-world optimisation problems;


Personal and Key Transferable/ Employment Skills and Knowledge:
7 Read and digest research papers from conferences and journals;
8 Relate theoretical knowledge to practical concerns;
9 Conduct a research project including sound statistical analysis of experimental results, and contrast the results found with those expected given previously published material;
10 Tackle a significant technical problem, and communicate the results.

SYLLABUS PLAN - summary of the structure and academic content of the module

The module content will be delivered by a mixture of lectures, workshops and directed reading. Indicative topics to be covered in the module include:

  • The multi-objective optimisation task;
  • Quality measures in multi-objective optimisation;
  • Multi-objective optimisation of expensive problems; 
  • Machine learning and Multi-objective optimisation;
  • Multi-objective optimisation of noisy problems;
  • Multi-objective optimisation of robust problems;
  • Multi-objective optimisation of dynamic problems;
  • Computational efficiency considerations in multi-objective optimisation;
  • Many- versus multi-objective optimisation;
  • Multi-objective cost/fitness landscapes;
  • Visualising multi-objective problems;
  • Preference incorporation in multi-objective optimisation;
  • Interactive multi-objective optimisation;
  • Hybridisation of evolutionary and multiple-criteria decision making.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 38 Guided Independent Study 112 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 20 Lectures
Scheduled learning and teaching activities 18 Workshops
Guided independent study 112 Independent study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
ELE quizzes 5-10 minute quizzes 1, 2 Quiz score, and discussion in workshops

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 40 Written Exams 60 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Exam  60 2 hours 1, 2, 4, 7, 8 Orally on request
Coursework - multi-objective optimisation project 40 50 Hours All Written, verbal

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Exam  Exam 1, 2, 4, 7, 8 Ref/Def Period
Coursework - multi-objective optimisation project Coursework All Ref/Def Period

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework and/or written exam in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

 

Web based and electronic resources:

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Coello Coello Carlos, Lamont Gary, Veldhuizen David, Evolutionary Algorithms for Solving Multi-objective Probelsm 2nd Springer 2007 978-0-387-33254-3
Set Carl Edward Rasmussen, Christopher K. I. Williams Gaussian Processes for Machine Learning MIT Press 2006 978-0262182539
Set Deb, K Multi-Objective Optimization using Evolutionary Algorithms Wiley 2000
Set Branke, J., Deb, K., Miettinen, K., Slowinski, R. (Eds.) Multiobjective Optimization: Interactive and Evolutionary Approaches Springer 2008
Set Miettinen, Kaisa Nonlinear Multiobjective Optimization Kluwer Academic Publishers 1999
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Wednesday 13th March 2024 LAST REVISION DATE Thursday 16th May 2024
KEY WORDS SEARCH None Defined

Please note that all modules are subject to change, please get in touch if you have any questions about this module.