Comparer des méthodes

	Harmony Search	Recuit simulé	Recherche Tabou
Domaine	Optimisation	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	2001	1983	1989
Auteur d'origine	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan	—	Fred Glover
Type	Metaheuristic population-based optimization	Probabilistic metaheuristic / local search	Local-search metaheuristic
Source fondatrice	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.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Alias	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Apparentées	5	5	4
Résumé	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.	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.