The course starts by addressing the ill-structured
problems and need for computational intelligence methods. It introduces
the concepts of heuristics and their use in conjunction with search
methods, solving problems using heuristics and metaheuristics, constraints
satisfaction. The course also introduces the concepts of cooperation
and adaptations and how they are influencing new methods for solving
complex problems. The course starts by illustrating how the concepts
of cooperation and adaptation are manifested in nature and how such
models are inspiring new types of solutions methods. Topics to be covered
include: search algorithms, game playing, constraints satisfaction,
meta-heuristics, evolutionary computing methods, swarm intelligence,
ant-colony algorithms, particle swarm methods, adaptive and learning
algorithms and the use of these algorithms in solving continuous and
discrete problems that arise in engineering applications.
The course is divided into three major sections that cover different cooperative
and adaptive techniques and their applications in different areas of engineering.
Metaheuristic algorithms can be classified into trajectory-based and population-based
algorithms. A trajectory-based metaheuristic algorithm such as simulated
annealing use a single agent or solution which moves through the design
space or search space in a piecewise style. A better move or solution
is always accepted, while a not-so-good move can be accepted with certain
probability. The steps or moves trace a trajectory in the search space,
with a non-zero probability that this trajectory can reach the global
optimum. On the other hand, population-based algorithms such as genetic
algorithms, ant colony optimization and particle swarm optimization use
multiple agents to search for an optimal or near-optimal solution.
- Optimization Theory and Combinatorics
- Graph-search Algorithms
- Game Playing as Search
- Tabu Search
- Simulated Annealing
- Evolutionary Computation
- Genetic Algorithms
- Swarm Intelligence
- Particle Swarm Optimization
- Ant Colony Optimization
Course Instructor: Alaa
Office hours: Fridays 7:00-8:20PM
Office hours: Thursdays 4:00-5:00PM
Keyvan Golestan Irani
Office hours: Mondays 4:00-5:00PM
- A. Engelbrecht. Fundamentals of Computational Swarm Intelligence.
- Xin-SheYang. Engineering Optimization: An Introduction with Metaheuristic
Applications. A JOHN WILEY & SONS, INC., 2010.
- Singiresu S. Rao. Engineering Optimization: Theory and Practice.
A John Wiley & Sons, INC., 2009.
- C. Revees. Modern Heuristic Techniques for Combinatorial Problems.
Halsted Press, New York, 1993.
Lectures will be based mainly, but not exclusively, on material in these textbooks and other resources. Lectures will not follow the same sequence as the material presented in the texts.