COURSE OUTLINE
CS-548
Special topic: Swarm Intelligence
Fall 2015
Course Objectives:
Swarm Intelligence is a special topic in Optimization, and therefore, it is about solving difficult optimization problems (such as NP-hard) using a specific distributed computational paradigm known as Swarm Intelligence (SI). It is a collection of operational paradigms that approaches a stable distributed system as a self-organized cluster of multiple agents or boids, natural or artificial, manifesting emergent behavior. Although the primary focus of SI is seen in this specific tonality, new algorithmic approaches from sciences and engineering to address complex optimization domains would be considered in this presentation. How simple local agent-agent and agent-environment interactions could lead to a stable collective global intelligent behavior for the entire system, unknown to individual agents, might precipitate would be the issue we’d focus upon. The two distinct paradigms we’d be leaning toward under the common banner of SI would be: Ant Colony Algorithms (ACA), and Particle Swarm Algorithms (PSA). Using these we would explore what has been attempted in the industry and in the scientific community. Though the entire course work would be predominantly theoretical, students would be encouraged to develop relevant application modules using the concepts discussed in the class using MATLAB or similar numerical platform. The focus is on learning (appreciating) the phenomenon of emergence using swarm intelligence.
Required textbook: None
Recommended books:
■
, Kennedy, Eberhart, and Shi
Morgan Kaufmann Publishers. (would be referred as SI)
■
■
, Sean Luke
Birkhäuser (would be referred as DS)
, Stephen Lynch
External lecture resources: http://iridia.ulb.ac.be/~soliveira/downloads/aco/Slides_intro_swarm_intelligence_2010.pdf
http://natcomp.liacs.nl
(a number of lecture notes from this site would be used) http://ocw.mit.edu/courses/engineering-systems-division/esd-77-multidisciplinary-system-designoptimization-spring-2010/lecture-notes/MITESD_77S10_lec12.pdf
(MIT Open Courseware)
Class website: http://web.cs.sunyit.edu/~sengupta/swarm/swarm.html
Instructor: Sam Sengupta, Room C129, 792-7353 (office), email address: kababs@gmail.com
Office hours: Mondays and Thursday 11:00-2:00 PM

Lecture topics:
1. Introduction to Swarm Intelligence (SI). A perspective.
2. System complexity, and optimization by trial and error.
3. Some standard Optimization tools
4. Evolutionary paradigm
5. Humanity and Social thinking
6. The particle Swarm and Collective Intelligence
7. Variation of Particle-swarm paradigm
8. Applications and models
Course presentation and delivery mode: Primarily it would be via lectures. Each student is expected to present two well-researched topics in the class in a group-mode. Each student is expected to participate actively in the class. From time to time, groups would be assigned homework for better understanding of the issues in our topics of interest.
Evaluation scheme: There would be two assignments, two exams and two presentations. The assignments and presentations would be in a group mode for each student in the class. The average over all these categories would be regarded as the final grade.
Students with disabilities:
Accommodations for Students with Disabilities registered at SUNY Polytechnic Institute
In compliance with the Americans with Disabilities Act of 1990 and with Section 504 of the
Rehabilitation Act, SUNY Polytechnic Institute is committed to ensuring educational access and accommodations for all its registered students seeking access to meet course requirements and fully participate in programs or activities. SUNY Poly students with documented disabilities and medical conditions are encouraged to request these services by registering with the Disability
Services Office and discussing their need for accommodations. For information or an appointment contact Suzanne Sprague, Disability Services Coordinator, at the Disability Services
Office, located in room B101 Kunsela Hall or by phone (315) 792-7170 ; or email
Suzanne.sprague@sunyit.edu
.
Course outcome: Students should be able to write their optimization and search algorithms using
● Standard Optimization tools
● Genetic algorithms
● Particle-swarm intelligence
● AL oriented designs