
Metaheuristic Optimization	Code: M0.536 : 6

View general information Description The subject within the syllabus as a whole Professional fields to which it applies Prior knowledge Learning objectives and results Content View the UOC learning resources used in the subject Guidelines on assessment at the UOC View the assessment model

This is the course plan for the second semester of the academic year 2023/2024. To check whether the course is being run this semester, go to the Virtual Campus section More UOC / The University / Programmes of study section on Campus. Once teaching starts, you'll be able to find it in the classroom. The course plan may be subject to change.

Description

Welcome to the Metaheuristic Optimization course, which combines concepts from Artificial Intelligence, Operations Research, Computer Science, and Industrial Engineering to develop intelligent algorithms and methods capable to tackle large-scale and NP-hard combinatorial optimization problems, even in scenarios where stochastic or dynamic conditions are considered (as it frequently happens in many real-life applications).

The course is based on the many years of research and transfer activities developed by the ICSO Meta team (https://icso.webs.upv.es). During these years, we have been able to develop different types of x-heuristic algorithms, including: biased-randomized heuristics, simheuristics, learnheuristics, discrete-event heuristics, and agile-optimization algorithms.

The subject within the syllabus as a whole

Due to the interdisciplinary nature of metaheuristic algorithms, and their noticeable capacities for solving optimization challenges in many application fields, this course is related to many others in the master. In particular, it is strongly related to the Simulation and Operations Research courses.

Professional fields to which it applies

The need for optimization of processes and systems is all around us: from transportation and logistics systems, to telecommunication networks, manufacturing facilities, smart cities, or insurance policies. Hence, x-heuristic algorithms are employed in many transfer projects with industrial and business partners. In addition, it is still a young research field with an extraordinary potential for obtaining sound results and publish them in international journals.

Prior knowledge

Some analytical skills and a strong wish to learn more about optimization algorithms are required. Also, the ability to read scientific documents in English, as well as basic programming and statistical / mathematical concepts and skills.

Learning objectives and results

The main goals of the course are:

Introduce students into the area of intelligent algorithms in combinatorial optimization.
Discover some of the most popular optimization challenges and application areas.
Learn the main types of x-heuristic algorithms that can be employed to tackle these challenges.
Design and develop algorithms for the problems studied, analyzing their behavior as well.

After completing the course, students should be able to:

Understand the main concepts of intelligent algorithms in combinatorial optimization.
Know the main types of x-heuristics that can be employed to solve NP-hard and large-scale combinatorial optimization problems.
Design and develop algorithms for some of the most studied combinatorial optimization problems.
Know how to analyze the results obtained by the methods developed, making comparisons to evaluate their efficiency.
Understand the many applications of these algorithms in real-life industrial and business sectors.
Understand the main ideas described in related scientific articles.

Among the master's competencies, this course will allow you to acquire the following:

Understand and apply advanced computing knowledge and numerical or computational methods to engineering problems.
Apply computational, mathematical and statistical methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.
Apply mathematical and computational methods to solve technological problems and company engineering problems, particularly in research, development and innovation tasks.
Ability to model problems using the language of mathematics and solve them with formal reasoning.
Identify the mathematical theories needed to construct models based on problems from other disciplines.
Handle mathematics and statistics software.
Model, simulate and analyze systems, processes and networks.

Content

Intro to Optimization and X-Heuristics
Random Search and VRPs
Biased-Randomized Algorithms (BRAs)
GRASP and TOPs
ILS and PFSPs
Recent Applications of BRAs
Simulation
Simheuristics
Learnheuristics and Agile Optimization
Genetic Algorithms I
Genetic Algorithms II
Original Short Article

Guidelines on assessment at the UOC

The assessment process is based on students' own work and the assumption that this work is original and has been carried out by them.

In assessment activities, the following irregular behaviours, among others, may have serious academic and disciplinary consequences: someone else being involved in carrying out the student's assessment test or activity, or the work being not entirely original; copying another's work or committing plagiarism; attempting to cheat to obtain better academic results; collaborating in, covering up or encouraging copying; or using unauthorized material, software or devices during assessment.

If students are caught engaging in any of these irregular behaviours, they may receive a fail mark (D/0) for the assessable activities set out in the course plan (including the final tests) or in the final mark for the course. This could be because they have used unauthorized materials, software or devices (e.g. social networking sites or internet search engines) during the tests, because they have copied text fragments from an external source (internet, notes, books, articles, other student's projects or activities, etc.) without correctly citing the source, or because they have engaged in any other irregular conduct.

In accordance with the UOC's academic regulations , irregular conduct during assessment, besides leading to a failing mark for the course, may be grounds for disciplinary proceedings and, where appropriate, the corresponding punishment, as established in the UOC's coexistence regulations.

In its assessment process, the UOC reserves the right to:

Ask the student to provide proof of their identity, as established in the university's academic regulations.
Request that students provide evidence of the authorship of their work, throughout the assessment process, both in continuous and final assessment, by means of an oral test or by whatever other synchronous or asynchronous means the UOC specifies. These means will check students' knowledge and competencies to verify authorship of their work, and under no circumstances will they constitute a second assessment. If it is not possible to guarantee the student's authorship, they will receive a D grade in the case of continuous assessment or a Fail in the case of final assessment.
For this purpose, the UOC may require that students use a microphone, webcam or other devices during the assessment process, in which case it will be the student's responsibility to check that such devices are working correctly.

View the assessment model

You can only pass the course if you participate in and pass the continuous assessment. Your final mark for the course will be the mark you received in the continuous assessment.