A metaheuristic is a higher-level strategy designed to guide other heuristics toward finding optimal solutions in optimization problems. While a heuristic addresses a specific problem, a metaheuristic is a general approach that can be adapted to different problems, guiding the search process to explore the solution space more effectively.
Here sare some examples of metaheuristics
Metaheuristics
Simulated Annealing (SA):
A probabilistic metaheuristic that explores the solution space by accepting worse solutions with decreasing probability as the “temperature” lowers. It mimics the physical process of cooling metals, helping avoid local minima and finding a near-optimal solution.
Genetic Algorithms (GA):
This population-based metaheuristic evolves solutions over generations through selection, crossover, and mutation processes. Genetic algorithms explore multiple solutions in parallel, balancing between exploitation and exploration to find near-optimal solutions for TSP.
These methods are particularly useful in large-scale instances of the TSP where exact algorithms become computationally impractical.