PhD Research Topics Guidance in Metaheuristic Computing

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.