الگوريتمهاي مدرن در بهينهسازي
Heuristic Optimization Algorithms
فرمت پروپوزال پروژه نهایی درس
Hadi Nobahari, Room 317, 66164636, email@example.com
Sayyed Hamed Sayyedipour, Room 101, firstname.lastname@example.org
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.
1- Introduction, Definitions and Concepts
Operational Research (OR)
Definition of an Optimization Problem
Classification of the Optimization Problems
Classification of the Optimization Techniques
Heuristic Algorithms vs. Metaheuristics
2- An Overview of Classical Optimization Techniques
3- An Overview of Heuristic Optimization Algorithms
Hill Climbing Methods
Ant Colony Optimization
Particle Swarm Optimization
Some Other Heuristics
4- More on Simulated Annealing
Real Annealing and Simulated Annealing
Simulated Annealing Algorithm
Convergence of Simulated Annealing
Continuous Simulated Annealing
One-loop Simulated Annealing
Top Applications of SA
Normalization of the Parameters
Evaluation of the Heuristic Algorithms
5- More on Evolutionary Algorithms
Drivers of Evolution
Steady State GAs
Top Applications of EA
Tuning of the Parameters
6- More on Tabu Search
Basic Tabu Search
Continuous Tabu Search
Diversification and Intensification
7- More on Ant Colony Optimization
Collective Behavior of Social Insects
Basic ACO Algorithms
Pheromones and Memory
Ant Algorithms for TSP
Adaptation to Continuous Problems
8- More on Particle Swarm Optimization
Canonical PSO Algorithm
9- Extensions of Heuristic Algorithms
Parallelization of the Optimization Algorithms
Heuristics to handle constraints
Handling Dynamic Optimization Problems
Handling Noisy Problems
Handling Expensive Cost Functions
Upon completion of this course the students will be able to:
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.
5+1 out of 20+2
Computer based Assignments
5 out of 20+2
10 out of 20+2
Term Project + Presentation
+0.5 out of 20+2
Early Definition of Term Projects
+0.5 out of 20+2
Early Submit of Term Projects
CEC benchmarks: http://www3.ntu.edu.sg/home/EPNSugan/index_files/cec-benchmarking.htm