Aerospace Engineering (master program)

TRAINING MODULE: Artificial Intelligence Based Optimization in Aerospace Engineering

General description

Format: On site
Institutions: Rzeszów University of Technology (Poland)

EQF level: 7 (2nd semester of master degree)
Instructors: D. Sc. Ph.D. Leszek Rolka
Department: Department of Avionics and Control
Language of instruction: Polish (for Polish master degree students),

English (for Erasmus students)

Workload: 100 h: 15 h (lectures) + 15 h (workshops) + 70 h
Number of hours: 30 h
Course timeframe Summer semester (Feb-Jun’ 2022)
Typology: New module within existing program
Prerequisites: Mathematics (basic algebra)

Basic knowledge about computer tools used in engineering work

Problems solving

Course methodology lectures

project-based learning

The main educational goal The main educational goal of the subject is to acquire knowledge and skills in the field of modern methods and techniques called artificial intelligence (AI) systems and their creative application to solve optimization problems in the area of airspace engineering.

K1–K4 – Knowledge,
S1–S10 – Skills,
C1–C2 – Social competences.
Has knowledge on the basic concepts of decision-making and optimization theory (K1);
Has highly specialized knowledge on the methods of decision-making, and bio-inspired methods of optimization (K2);
Has highly specialized knowledge to identify optimization tasks that require application of non-classical optimization methods (K3);
Has highly specialized knowledge to describe optimization problems (K4);
Can identify optimization tasks that require application of non-classical optimization methods (S1);
Can describe optimization problems using various methods of knowledge representation (S2);
Can recognize the advantages and disadvantages of the used methods and obtained solutions (S3);
Can analyze the literature and publications in the field of intelligent decision-making systems (S4);
Can apply the topics learned during the lecture to solve practical optimization tasks (S5);
Can select and apply existing software tools to solve optimization tasks (S6);
Can propose a solution to a real-world optimization problem in aerospace engineering by using selected AI-based optimization tools (S7);
Can perform computer simulations, analyze and interpret the results, formulate conclusions (S8);
Can write a report including the problem analysis and the results obtained (S9);
Can perform scientific research in aerospace engineering (S10);
Can apply teamwork in solving optimization problems (C1);
Is aware of the responsibility for jointly performed tasks (C2).

1. Introduction to the theory of complex optimization: computational complexity, combinatorial problems, NP-hard problems (K1-K4).
2. Evolutionary algorithms I (K1-K4).
3. Evolutionary algorithms II (K1-K4).
4. Simulated annealing (K1-K4).
5. Ant colony and bee colony algorithms (K1-K4).
6. Particle swarm optimization (K1-K4).
7. Hybrid optimization methods (K1-K4).
8. Test (1 hour).

1. Introduction to the workshops: organization, set up of work teams, assignment of topics. Introduction to available software tools to be used for solving optimization tasks (S6).
2. Presentation of literature review on selected optimization methods and their real-world applications (S9).
3. Problem definition, presentation of projects concepts proposed by every work team (S1, S2, S3, S5, S7).
4. Mastering software used for solving the selected optimization task (S6).
5. Practical realization of the projects, discussion (S2, S5, S6, S8, S9, S10).
6. Presentation of the project reports and obtained results by every work team
(S8, S9, S10).
7. Summary.

Individual activity: 70 hours.

Job profile

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Military Engineer

Military engineers perform technical and scientific functions in the military, such as the development of concepts for military technical equipment, support of the manufacturing of military equipment, and technical research, maintenance, and quality assurance.
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Research engineer

Research engineers combine research skills and knowledge of engineering principles aiming to improve through research, processes, techniques, products, and systems at large.

IMPORTANT: This prototyped programme is EXCLUSIVE FOR partners of the ASSETs+ consortium and associated stakeholders. If you want to join the ASSETs+ Stakeholders Group and become part of our ecosystem, please, click here.