Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Optimisation | |||
| Reference | CM4140 | Version | 1 |
| Created | December 2023 | SCQF Level | SCQF 10 |
| Approved | April 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| This modules aims to introduce students to a range of techniques for modelling and solving optimisation problems, including well-known meta-heuristic techniques such as genetic algorithms and particle swarm optimisation. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Compose an appropriate encoding of an optimisation problem using standard representations. |
| 2 | Develop a number of well-know heuristic algorithms to solve an optimisation problem. |
| 3 | Test the effectiveness and efficiency of bio-inspired meta-heuristic algorithms for solving a given optimisation problem. |
| 4 | Examine the performance of extensions and modifications to standard optimisation algorithms. |
| 5 | Examine state-of-the-art research-led questions and approaches to optimisation. |
| Indicative Module Content |
|---|
| Fitness landscapes, fitness functions, neighbourhoods, encodings, exploration and exploitation, constrained optimisation. Random search, random walk, hill-climbing, tabu search, simulated annealing, iterated local search, Particle swarm optimisation (PSO), ant colony optimisation (ACO), artificial immune systems, evolutionary algorithms (EA), genetic algorithms (GA), estimation of distribution algorithm (EDA). Crossover, mutation and selection methods, elitism, parallelisation, hyper-heuristics. Research-led teaching such as multi-objective optimisation, surrogate modelling, dynamic optimisation, explainable optimisation, visualisation. |
| Module Delivery |
|---|
| This module is based on lectures supplemented with laboratory sessions, where practical skills will be developed to solve varied learning tasks and practical exercises. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4, 5 |
| Description: | A coursework in which the techniques studied on the course are used to study an optimisation problem. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of component 1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student must achieve an A in C1 |
| B | The student must achieve a B in C1 |
| C | The student must achieve a C in C1 |
| D | The student must achieve a D in C1 |
| E | The student must achieve an E in C1 |
| F | The student must achieve an F in C1 |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | An Introduction to Metaheuristics for Optimization. Chopard, Bastien; Tomassini, Marco. 2018 |
| 2 | Representations for genetic and evolutionary algorithms Rothlauf, Franz, 2006 |
| 3 | Evolutionary and swarm intelligence algorithms Bansal, Jagdish Chand, editor.; Siṃha, Pramoda Kumāra, editor.; Pal, Nikhil R., editor. 2019 |
| 4 | Evolutionary algorithms and neural networks : theory and applications Mirjalili, Seyedali, 2019 |
| 5 | Introduction to evolutionary algorithms Yu, Xinjie.; Gen, Mitsuo, 2010 |