Comparar métodos

	Búsqueda de Vecindad Variable (VNS)	Algoritmo Genético
Campo	Optimización	Optimización
Familia	Process / pipeline	Process / pipeline
Año de origen	1997	1975
Autor original	—	John Henry Holland
Tipo	Metaheuristic — neighborhood-based	Population-based metaheuristic
Fuente seminal	Mladenović, N. & Hansen, P. (1997). Variable Neighborhood Search. Computers & Operations Research, 24(11), 1097–1100.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Alias	VNS, Değişken Komşuluk Araması (VNS), variable neighbourhood search	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Relacionados	4	5
Resumen	Variable Neighborhood Search (VNS) is a metaheuristic optimization framework introduced by Mladenović and Hansen in 1997. It escapes local optima by systematically switching among a predefined set of neighborhood structures — first perturbing the current solution (shaking) to reach a different region of the search space, then applying a local search within that region, and finally accepting the new solution only if it improves the incumbent. The method is flexible enough to handle combinatorial problems (routing, scheduling, graph problems) as well as continuous optimization, making it one of the most widely used neighborhood-based metaheuristics in operations research.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.