Compara mètodes

	Algorisme genètic	Harmony Search	Cerca Tabu
Camp	Optimització	Optimització	Optimització
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	1975	2001	1989
Autor original	John Henry Holland	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan	Fred Glover
Tipus	Population-based metaheuristic	Metaheuristic population-based optimization	Local-search metaheuristic
Font seminal	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.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Àlies	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Relacionats	5	5	4
Resum	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.	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.