الگوريتم‌هاي مدرن در بهينه‌سازي

Heuristic Optimization Algorithms

45-770

Spring 2016

 

فرمت پروپوزال پروژه نهایی درس

 

Instructor:

Hadi Nobahari, Room 317, 66164636, nobahari@sharif.edu

Teaching Assistant:

Sayyed Hamed Sayyedipour, Room 101, seyedipour_seyedhamed@ae.sharif.edu

Course Objectives

In this graduate course the modern heuristic optimization algorithms such as Evolutionary Algorithms, Ant Colony Optimization, Simulated Annealing, Tabu Search and Particle Swarm Optimization are introduced with a concentration on the application of these algorithms. The course begins with a classification of the optimization problems and the definition of the primary concepts such as discrete and continuous search domains, multi-objective optimization, dynamic optimization, global optimization, stochastic optimization, swarm intelligence, etc. Then some of the well-known heuristic methods are introduced in detail including the basic and original algorithms, characteristics, adaptation to constrained and multi-objective problems and parallelization. The course ends with some miscellaneous and complementary materials.

 Syllabus:

1-      Introduction, Definitions and Concepts

2-      An Overview of Classical Optimization Techniques

3-      An Overview of Heuristic Optimization Algorithms

4-      More on Simulated Annealing

5-      More on Evolutionary Algorithms

6-      More on Tabu Search

7-      More on Ant Colony Optimization

8-      More on Particle Swarm Optimization

9-      Extensions of Heuristic Algorithms

Course outcome:

Upon completion of this course the students will be able to:

  1. Utilize state of the art heuristic optimization algorithms in their research activities.
  2. Design and propose new and hybrid optimization algorithms.
  3. Customize heuristic optimization algorithms for special applications

Text Books:

1-      Z. Michalewicz and D. B. Fogel, "How to Solve it: Modern Heuristics", Springer, 1999.

2-      J. Dreo, P. Siarry, A. Petrowski and E. Taillard , "Metaheuristics for Hard Optimization", Springer, 2005.

3-      S. N. Sivanandam and S. N. Deepa, "Introduction to Genetic Algorithms", Springer, 2008.

4-      R. L. Haupt and S. E. Haupt, "Practical Genetic Algorithms", 2nd edition, Wiley Interscience, 2004.

5-      D. Simon, “Evolutionary Optimization Algorithms”, Wiley, 2013.

6-      Fred Glover, Manuel Laguna, "Tabu Search", Klawer Academic Publishers, Norwell, MA, 1997.

7-      M. Dorigo and T. Stutzle, "Ant Colony Optimization", MIT Press, 2004.

8-      M. Clerc, "Particle Swarm Optimization", ISTE Ltd, 2006.

9-      S. S. Rao, "Engineering Optimization: Theory and Practice", 3rd edition, John Wiley & Sons, Inc., 1996.

 

Grading

5+1 out of 20+2

Computer based Assignments

5 out of 20+2

Final Exam

10 out of 20+2

Term Project + Presentation

+0.5 out of 20+2

Early Definition of Term Projects (Bonus)

+0.5 out of 20+2

Early Submit of Term Projects (Bonus)

 

CEC benchmarks: http://www3.ntu.edu.sg/home/EPNSugan/index_files/cec-benchmarking.htm