OR 6500: Metaheuristics and Applications
Lecture - 4 credits
- Focuses on solving large combinatorial optimization problems.
- Metaheuristic search aims to find a "very good" solution that satisfies the problem constraints.
- Describes multiple metaheuristic search methods such as simulated annealing (SA), tabu search (TS), genetic algorithms (GA), particle swarm optimization (PSO), and multiobjective methods.
- Uses algorithms to find values of discrete and/or continuous variables that optimize a system’s performance.
- Discusses the application of metaheuristics to a variety of different problems, including hub location allocation, parallel machine scheduling, travelling salesman problem (TSP), curve fitting, clustering, n-queen, min one, etc.
- Incorporates practical experiments to demonstrate the advantages and disadvantages of metaheuristic search methods for different applications.
Focuses on solving large combinatorial optimization problems. Show more.