Module Details

Module Code: COMP9058
Title: Metaheuristic Optimisation
Long Title: Metaheuristic Optimisation
NFQ Level: Expert
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 2 programme(s)
Module Description: This module explores techniques for the analysis and design of efficient techniques to solve real-life problems. In this module the learner will be introduced to the complexity of solving hard combinatorial problems, i.e., recognise and prove NP-hard problems. Additionally, the module covers effective and efficient meta-heuristic techniques to tackle complex decision problems, especially combinatorial optimisation problems.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Categorise a real-life problem with respect to its computational complexity.
LO2 Assess the benefits and limitations of meta-heuristics to solve NP-hard problems.
LO3 Solve an NP-hard problem with meta-heuristics to find a satisfactory lower-bound solution.
LO4 Analyse the average performance of a randomised algorithm to solve an NP-hard problem
LO5 Apply nature-inspired and local search meta-heuristics to solve real-life problems.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Computational Complexity Theory
Complexity classes (P, NP, NP-complete, and NP-hard); P vs. NP; polynomial-time reductions to prove NP-completeness; tractability and intractability; the no free lunch theorem.
Population-based meta-heuristics
Mainstream population-based meta-algorithms such as: evolutionary and genetic algorithms, estimation of distribution algorithms (EDAs); ant-colony optimization, particle swarm optimization, and artificial bee colony algorithm
Single solution-based meta-heuristics
Application of standard local search techniques such as: neighborhood search, variable neighborhood search, hill climbing, simulated annealing, and Tabu search; global Vs. local optimum solutions
Randomised Algorithms
Las Vegas and Monte Carlo algorithms; k-opt and Lin-Kernighan algorithms; random walk; randomised search trees; randomised sorting.
Performance of randomised algorithms
Random variables and their properties; average case-runtime of Las Vegas algorithms; runtime distributions of las Vegas algorithm; evaluate and compare randomised algorithms.
Applications
Applying population-based and single solution-based meta-heuristics to solve real-world problems , e.g., assignment problem, Boolean satisfiability problem, traveling salesman problem, and knapsack problem
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
In this project the students will be given a problem and they will have to show that the problem is NP-complete and implement a population-based meta-heuristic, to solve the problem and critically evaluate the performance the solution.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 3,4,5
Assessment Description
In this project the students will be given a real-life problem and the students will have to implement a single solution-based metaheuristic to solve the problem and critically evaluate the performance of the solution
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Presentation of theory. Every Week 2.00 2
Lab Contact Lab supporting lectures. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Presentation of theory. Every Week 2.00 2
Lab Contact Lab supporting lectures. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Stuart Russell and Peter Norvig. (2016), Artificial Intelligence: A Modern Approach, Pearson Education Limited, [ISBN: 9781292153964].
  • El-Ghazali Talbi. (2009), Metaheuristics: From Design to Implementation, John Wiley & Sons, [ISBN: 978-0-470-278].
  • Xin-She Yang. (2010), Nature-Inspired Metaheuristic Algorithms, 2. Luniver Press, [ISBN: 9781905986286].
Supplementary Book Resources
  • Steve S. Skiena. (2009), The Algorithm Design Manual, 2nd Edition. Springer, [ISBN: 9781848000698].
  • Holger H. Hoos and Thomas Stützle. (2004), Stochastic Local Search: Foundations & Applications, Morgan Kaufmann, [ISBN: 978-149330373].
  • Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 2009. (2009), Introduction to Algorithms, 3rd Edition. MIT Press, [ISBN: 9780262033848].
Supplementary Article/Paper Resources
  • Holger H. Hoos and Thomas Stutzle. (1998), Evaluating Las Vegas Algorithms: Pitfalls and Remedies, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence.
  • Stephen A. Cook. (1971), The Complexity of Theorem-Proving Procedures, Proceedings of the Third Annual ACM Symposium on Theory of Computing.
  • Keld Helsgaun. (2009), General k-opt submoves for the Lin-Kernighan TSP heuristic, Math. Program. Comput, 1.
  • Charlotte Truchet, Alejandro Arbelaez, Florian Richoux, Philippe Codognet. (2016), Estimating parallel runtimes for randomized algorithms in constraint solving, Journal Of Heuristics, 22.
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 1 Mandatory
CR_KSADE_9 Master of Science in Software Architecture & Design 2 Group Elective 1