Сравнение методов

	Переменные окрестности (VNS)	Harmony Search	Имитация отжига
Область	Оптимизация	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1997	2001	1983
Автор метода	—	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan	—
Тип	Metaheuristic — neighborhood-based	Metaheuristic population-based optimization	Probabilistic metaheuristic / local search
Основополагающий источник	Mladenović, N. & Hansen, P. (1997). Variable Neighborhood Search. Computers & Operations Research, 24(11), 1097–1100.	Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68.	Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
Другие названия	VNS, Değişken Komşuluk Araması (VNS), variable neighbourhood search	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Связанные	4	5	5
Сводка	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.	Harmony Search (HS) is a population-based metaheuristic optimization algorithm introduced by Geem, Kim, and Loganathan in 2001. It mimics the improvisation process of jazz musicians seeking a perfect state of harmony, using three operators — memory consideration, pitch adjustment, and random selection — to generate candidate solutions. The algorithm applies to both continuous and discrete variables and has found wide use in engineering design, water distribution network optimization, and combinatorial problems.	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.