40.614 Metaheuristics Optimization
40.015 Course Description
Real-world optimisation problems are often nonlinear, multimodal and with various complex constraints. Traditional exact optimisation methods frequently fail to accurately obtain solutions for these problems in reasonable computation times. Metaheuristic optimisation methods are artificial intelligence search methods used in the design and management of a wide range of complex systems. The aim of these methods is not so much finding the optimal solution for a given problem, but to solve rapidly large and realistic instances while ensuring good quality of the solutions. Metaheuristics are commonly used in many research applications and industries, such as supply chain, logistics, transportation, aviation, urban planning, energy, water resources, robotics, etc. This course describes a variety of metaheuristic search methods including, simulated annealing, tabu search, ant colony, particle swarm, genetic algorithms, and multi-objective metaheuristics. Real case studies are presented to emphasize the solution process and performance analysis of the various metaheuristic methods.
Lectures
-Lecture 1: Introduction to Metaheuristic Optimization — slides
-Lecture 2: Exact Methods of Optimization — slides
-Lecture 3: Local Search — slides
-Lecture 4: Solution Enconding & Move Operators — slides
-Lecture 5: Simulated Annealing — slides
-Lecture 6: Variable Neighborhood Search & Greedy Heuristics — slides
-Lecture 7: Tabu Search — slides
-Lecture 8: Common Concepts for Metaheuristics — slides
-Lecture 9: Very Large Neighborhood Search — slides
-Lecture 10: Evolutionary Algorithms — slides
-Lecture 11: Project Consultation —
-Lecture 12: Types of Evolutionary Algorithms — slides
-Lecture 13: Using Genetic Algorithms to Calibrate Neural Networks — slides
-Lecture 14: Genetic Programming — slides
-Lecture 15: Multi-objective Optimization — slides
-Lecture 16: Non-Dominated Sorting Genetic Algorithm — (NSGA-II) slides
-Lecture 17: Particle Swarm Optimization — slides
-Lecture 18: Ant Colony Optimization — slides
-Lecture 19: Other Swarm Optimization Algorithms — slides