metaheuristilised
Metaheuristics are high-level problem-independent strategies for guiding the search for near-optimal solutions in complex optimization problems. They operate by iteratively exploring a search space and applying problem-specific heuristics within a general framework, rather than attempting to compute an exact optimum. The term combines “meta” (a higher-level approach) with “heuristic” (rule-of-thumb methods).
Typical metaheuristics balance exploration of new regions with exploitation of promising areas and often include stochastic
Major families include evolutionary algorithms (such as genetic algorithms and memetic algorithms), swarm-based methods (particle swarm
Applications span logistics and scheduling, vehicle routing, resource allocation, network design, engineering optimization, and machine learning
Limitations include lack of guaranteed optimality, sensitivity to parameter settings, and sometimes high computational cost for