Σύγκριση μεθόδων

	Προσομοίωση Ανόπτησης με Ντετερμινιστικές Μεταβολές	Προσομοιωμένη Ανόπτηση	Αναζήτηση Tabu
Πεδίο	Προσομοίωση	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	1990	1983	1989
Δημιουργός	Rose, K., Gurewitz, E., Fox, G. C.	—	Fred Glover
Τύπος	Deterministic metaheuristic — annealing schedule without probabilistic acceptance	Probabilistic metaheuristic / local search	Local-search metaheuristic
Θεμελιώδης πηγή	Rose, K., Gurewitz, E., Fox, G. C. (1990). A deterministic annealing approach to clustering. Pattern Recognition Letters, 11(9), 589-594.	Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Εναλλακτικές ονομασίες	DSA, Deterministic Annealing, Greedy Annealing, Temperature-Scheduled Descent	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Συναφείς	2	5	4
Σύνοψη	Deterministic Simulated Annealing (DSA) is an optimization metaheuristic that adopts the cooling-schedule structure of classical simulated annealing but replaces the probabilistic Metropolis acceptance criterion with a strictly deterministic rule: only improving moves are accepted. This yields a reproducible, greedy-descent procedure guided by an annealing temperature schedule.	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.	Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.