Σύγκριση μεθόδων

	Μεταευρετική Μεταβλητής Γειτονιάς (Variable Neighborhood Search - VNS)	Γενετικός Αλγόριθμος	Αναζήτηση Αρμονίας	Προσομοιωμένη Ανόπτηση
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	1997	1975	2001	1983
Δημιουργός	—	John Henry Holland	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan	—
Τύπος	Metaheuristic — neighborhood-based	Population-based metaheuristic	Metaheuristic population-based optimization	Probabilistic metaheuristic / local search
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	VNS, Değişken Komşuluk Araması (VNS), variable neighbourhood search	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Συναφείς	4	5	5	5
Σύνοψη	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.	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.