Multi-Objective Optimisation and Decision Making - 2025 entry

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. 

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

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.

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.

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.

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.

Desdeo framework: 

• https://desdeo.misitano.xyz/about

Python libraries: 

• https://deap.readthedocs.io/en/master

• https://pymoo.org/operators/index.html

• https://desdeo.misitano.xyz

• https://github.com/jMetal/jMetalPy

• https://platypus.readthedocs.io/en/latest/#

• https://esa.github.io/pygmo  

Reading list for this module: