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| Harmony Search× | Myrekolonioptimering× | |
|---|---|---|
| Fagområde | Optimering | Optimering |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2001 | 1992 (foundational thesis); 1997 (Ant Colony System formalization) |
| Ophavsperson≠ | Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan | — |
| Type≠ | Metaheuristic population-based optimization | Metaheuristic — swarm intelligence |
| Oprindelig kilde≠ | Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68. DOI ↗ | Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66. DOI ↗ |
| Aliasser | HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization | ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. | Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling. |
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