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Heuristic Optimization Algorithms

45-770

Spring 2021

Sundays and Tuesdays: 13:30-15:00 am

Online Link: https://vc.sharif.edu/ch/nobahari

فرمت پروپوزال پروژه نهایی درس

Instructor:

Hadi Nobahari, Room 317, 66164636, nobahari at sharif dot edu

Teaching Assistant:

Meysam Alizad, m.alizad.ab(at)gmail(dot)com

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

• Optimization

• Operational Research (OR)

• Engineering Optimization

• Definition of an Optimization Problem

• Feasibility Problem

• Classification of the Optimization Problems

• Classification of the Optimization Techniques

• Heuristic Algorithms vs. Metaheuristics

• Swarm Intelligence

• Population-Based Optimization

• Multi-objective Optimization

• Parallelization

• Evaluation of the Optimization Algorithms

2-      An Overview of Classical Optimization Techniques

• Linear programming

• Nonlinear Programming

3-      An Overview of Heuristic Optimization Algorithms

• Neighborhood Search

• Hill Climbing Methods

• Greedy Algorithms

• Simulated Annealing

• Evolutionary Algorithms

• Tabu Search

• Ant Colony Optimization

• Particle Swarm Optimization

• Some Other Heuristics

4-      More on Simulated Annealing

• Real Annealing and Simulated Annealing

• Metropolis Algorithm

• Simulated Annealing Algorithm

• Continuous Simulated Annealing

• One-loop Simulated Annealing

• Temperature Scheduling

• Convergence of Simulated Annealing

• Applications

• Normalization of the Parameters

• Tuning the Parameters of an algorithm

5-      More on Evolutionary Algorithms

• Methods of encoding

• Operators of Evolution

• Models of Evolution

• Genetic Algorithms

• Steady State GAs

• Genetic Programming

• Memetic Algorithms

• Differential Evolution

6-      More on Tabu Search

• Basic Tabu Search

• Short-term Memory

• Long-term Memory

• Diversification and Intensification

• Continuous Tabu Search

7-      More on Ant Colony Optimization

• Collective Behavior of Social Insects

• Basic ACO Algorithms

• Ant Algorithms for TSP

• Adaptation to Continuous Problems

• Applications

8-      More on Particle Swarm Optimization

• Canonical PSO Algorithm

• Important Parameters

• Neighborhood Topologies

9-      Extensions of Heuristic Algorithms

• Multi-objective Algorithms

• Parallelization of the Optimization Algorithms

• Heuristics to handle constraints

• Handling Dynamic Optimization Problems

• Handling Noisy Problems

• Handling Expensive Cost Functions

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, 2004.

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

3-      El-Ghazali Talbi, Metaheuristics: From Design to Implementation, Wiley, 2009.

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

5-      Xin She Yang, Nature-inspired Metaheuristic Algorithms, Luniver Press, 2014.

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

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

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

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

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

 

Grading

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