So sánh phương pháp

	Tìm kiếm lân cận biến đổi (VNS)	Thuật toán di truyền	Annealing mô phỏng
Lĩnh vực	Tối ưu hóa	Tối ưu hóa	Tối ưu hóa
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	1997	1975	1983
Người khởi xướng	—	John Henry Holland	—
Loại	Metaheuristic — neighborhood-based	Population-based metaheuristic	Probabilistic metaheuristic / local search
Công trình gốc	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.	Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
Tên gọi khác	VNS, Değişken Komşuluk Araması (VNS), variable neighbourhood search	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Liên quan	4	5	5
Tóm tắt	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.	Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.