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Information
| Code | CENG007 |
| Name | Advanced Swarm Intelligence |
| Term | 2024-2025 Academic Year |
| Term | Fall |
| 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 Instructor |
1 |
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 | ||