Optimization techniques are search methods, where
the goal is to find a solution to an optimization problem, such
that a given quantity is optimized, possibly subject to a set
of constraints. Modern optimization techniques start to demonstrate
their power in dealing with hard optimization problems in robotics
and automation such as manufacturing cells formation, robot motion
planning, job scheduling, cell assignment, vehicle routing problem,
assembly line balancing, shortest sequence planning, sensor placement,
unmanned-aerial vehicles (UAV) communication relaying and multirobot
coordination to name just a few. These novel techniques are nature-inspired
stochastic optimization methods that iteratively use random elements
to transfer one candidate solution into a new, hopefully better,
solution with regard to a given measure of quality. Using a multi-disciplinary
perspective and reviewing fundamental theories, this course provides
a comprehensive introduction to metaheuristic or stochastic optimization
and highlights the power of these computational techniques in
solving complex problems inrobotics and automation. The course
starts by introducing optimization theory and illustrating how
the concepts of complex decentralized optimization are manifested
in nature and how such models are inspiring new types of solutions
methods. Topics to be covered include: tabu search, simulated
annealing, evolutionary computing methods, computational swarm
intelligence, ant-colony algorithms and particle swarm optimization.
Different in-depth case studies will be provided to learn how
to use these techniques in solving continuous and discrete problems
that arise in robotics and automation applications.
Metaheuristic algorithms can be classified into trajectory-based and population-based algorithms.
A trajectory-based metaheuristic optimization 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.
The course is divided into two seminars that cover different metaheuristic optimization techniques and their applications in robotics and automation.
Course Instructor: Dr.
I: Trajectory-based Metaheuristic Optimization
- Optimization Theory
- Tree-search Algorithms
- Tabu Search
- Simulated Annealing
II: Population-based Metaheuristic Optimization
- Nature-inspired Metaheuristic Optimization
- Evolutionary Computation
- Genetic Algorithms
- Swarm Intelligence
- Ant Colony Optimization
- Particle Swarm Optimization
- 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.
- K. Doerner, M. Gendreau, P. Greistorfer, W. Gautjahr, R. Hartl
and M. Reimann (Eds.). Meteheuristics: Progress in Complex
Systems Optimization. Springer, 2007" K. Doerner, M. Gendreau,
P. Greistorfer, W. Gautjahr, R. Hartl and M. Reimann (Eds.). Meteheuristics:
Progress in Complex Systems Optimization. Springer, 2007.