Metaheuristic computing has emerged as a powerful solution for solving optimization problems efficiently.
The term ‘Metaheuristics’ combines ‘Meta’ (higher level) and ‘Heuristics’ (trial and error-based goal finding).
Metaheuristic algorithms solve complex optimization problems using local and global search techniques.
There are two main types: single solution-based algorithms (e.g., Tabu search, Simulated Annealing) and population-based algorithms (e.g., Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization).
These algorithms are widely used in engineering, transportation, social sciences, and business applications.
Computer Science and Engineering, Computer Science, Computer applications, Information Technology and Computer Networks
Computer Science and Engineering, Computer Science, Computer applications, Information Technology and Computer Networks