metaheuristics

Metaheuristics are high-level algorithmic frameworks: they guide a search process to find optimal or near-optimal solutions for complex, often NP-hard, optimization problems (e.g., the Traveling Salesman Problem) where exact methods are computationally infeasible.

This technology provides a robust, problem-independent strategy for tackling large-scale optimization challenges. Metaheuristics operate by balancing two critical components: exploration (diversification) of the entire solution space and exploitation (intensification) of promising regions. They do not guarantee a global optimum but consistently deliver high-quality, near-optimal solutions with reasonable computational effort. Key examples include population-based methods like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), alongside single-solution techniques such as Simulated Annealing (SA) and Tabu Search. We deploy these frameworks across logistics, scheduling, and design optimization when exact solutions fail to scale.

https://en.wikipedia.org/wiki/Metaheuristic