CENG007 Advanced Swarm Intelligence

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG007
Name Advanced Swarm Intelligence
Term 2022-2023 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Doç. Dr. MUSTAFA ORAL


Course Goal / Objective

To have knowledge of intelligent agents for modeling of industrial, social and biological systems.Have skills in developing simulation models based on swarms of intelligent agents.

Course Content

agent system modelling individual agents; Passive particle agents social agents; Flocking behaviour; Flocking behaviour applications; Particle swarm optimisation (PSO); path planning applications. PSO for path planning. ;Ant colony optimisation (ACO) Bees Colony algorithm; Evolutionary Agents (EA);;Selected topics: multi-objective optimisation

Course Precondition

None

Resources

Russell, Stuart J. ; Norvig, Peter, 2003 , Artificial Intelligence: A Modern Approach (2nd ed. )

Notes

Nilsson, Nils,1998 , Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Have knowledge of intelligent agents for modeling of industrial, social and biological systems.
LO02 Have knowledge of modeling of generic intelligent agents in complex landscapes.
LO03 Have knowledge of modeling of social agents in complex landscapes.
LO04 Have knowledge of the learning of intelligent agents in complex landscapes.
LO05 Have skills in using intelligent agents to solve optimization problems in complex landscapes.
LO06 Have skills in developing simulation models based on swarms of intelligent agents.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. 4
PLO02 Bilgi - Kuramsal, Olgusal By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. 3
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 4
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. 2
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. 3
PLO06 Yetkinlikler - Öğrenme Yetkinliği Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. 5
PLO07 Beceriler - Bilişsel, Uygulamalı Has the skills of learning. 3
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 4
PLO09 Beceriler - Bilişsel, Uygulamalı Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering.
PLO10 Beceriler - Bilişsel, Uygulamalı Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. 3
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 4
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities.


Week Plan

Week Topic Preparation Methods
1 Introduction to the course A basic coverage of the topics Reading the lecture notes Öğretim Yöntemleri:
Anlatım
2 agent system modelling Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Beyin Fırtınası
3 social agents Reading the lecture notes Öğretim Yöntemleri:
Anlatım
4 Particle swarm optimisation (PSO) Reading the lecture notes Öğretim Yöntemleri:
Anlatım
5 Ant colony optimisation (ACO) Reading the lecture notes Öğretim Yöntemleri:
Anlatım
6 Bee Colony Optimization Reading the lecture notes Öğretim Yöntemleri:
Anlatım
7 Gray Wolf Algorithm Reading the lecture notes Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam Exam preparation and project developing Ölçme Yöntemleri:
Yazılı Sınav, Ödev, Performans Değerlendirmesi
9 Selected topics : New Trends in Swarm Intelligence Research and reading course materials Öğretim Yöntemleri:
Alıştırma ve Uygulama, Grup Çalışması, Beyin Fırtınası
10 Selected topics :Evolution Based Algorithms Research and reading course materials Öğretim Yöntemleri:
Alıştırma ve Uygulama, Grup Çalışması, Beyin Fırtınası
11 Selected topics : Nature Based Algorithms Research and reading course materials Öğretim Yöntemleri:
Soru-Cevap, Alıştırma ve Uygulama, Beyin Fırtınası
12 Selected topics : Shape allocation Research and reading course materials Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Grup Çalışması, Beyin Fırtınası
13 Selected topics :multi-robot task allocation. Research and reading course materials Öğretim Yöntemleri:
Anlatım, Beyin Fırtınası
14 Selected topics:multi-robot path planning. Research and reading course materials Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Grup Çalışması, Beyin Fırtınası
15 Selected topics : Site Layout optimization Research and reading course materials Öğretim Yöntemleri:
Anlatım, Grup Çalışması, Beyin Fırtınası
16 Term Exams Project design and preparation for the presentation exam Ölçme Yöntemleri:
Proje / Tasarım, Performans Değerlendirmesi, Sözlü Sınav
17 Term Exams Project design and preparation for the presentation exam Ölçme Yöntemleri:
Proje / Tasarım, Sözlü Sınav, Performans Değerlendirmesi


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 18.11.2022 03:10