Beam-ACO

Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling. Ant colony optimization (ACO) is a metaheuristic approach to tackle hard combinatorial optimization problems. The basic component of ACO is a probabilistic solution construction mechanism. Due to its constructive nature, ACO can be regarded as a tree search method. Based on this observation, we hybridize the solution construction mechanism of ACO with beam search, which is a well-known tree search method. We call this approach Beam-ACO. The usefulness of Beam-ACO is demonstrated by its application to open shop scheduling (OSS). We experimentally show that Beam-ACO is a state-of-the-art method for OSS by comparing the obtained results to the best available methods on a wide range of benchmark instances.


References in zbMATH (referenced in 24 articles , 1 standard article )

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  1. Mejía, Gonzalo; Yuraszeck, Francisco: A self-tuning variable neighborhood search algorithm and an effective decoding scheme for open shop scheduling problems with travel/setup times (2020)
  2. Tang, Liang; Jin, Zhihong; Qin, Xuwei; Jing, Ke: Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design (2019)
  3. Zubaran, Tadeu K.; Ritt, Marcus: An effective heuristic algorithm for the partial shop scheduling problem (2018)
  4. Abdelmaguid, Tamer F.; Shalaby, Mohamed A.; Awwad, Mohamed A.: A tabu search approach for proportionate multiprocessor open shop scheduling (2014)
  5. Palacios, Juan; González-Rodríguez, Inés; Vela, Camino; Puente, Jorge: Robust swarm optimisation for fuzzy open shop scheduling (2014) ioport
  6. Thiruvady, Dhananjay; Ernst, Andreas; Wallace, Mark: A Lagrangian-ACO matheuristic for car sequencing (2014)
  7. Fernandez-Marquez, Jose Luis; Di Marzo Serugendo, Giovanna; Montagna, Sara; Viroli, Mirko; Arcos, Josep Lluis: Description and composition of bio-inspired design patterns: a complete overview (2013) ioport
  8. Chen, Ling; Sun, Hai-Ying; Wang, Shu: A parallel ant colony algorithm on massively parallel processors and its convergence analysis for the travelling salesman problem (2012)
  9. Rakrouki, Mohamed Ali; Ladhari, Talel; T’kindt, Vincent: Coupling genetic local search and recovering beam search algorithms for minimizing the total completion time in the single machine scheduling problem subject to release dates (2012)
  10. Martens, David; Baesens, Bart; Fawcett, Tom: Editorial survey: swarm intelligence for data mining (2011) ioport
  11. López-Ibáñez, Manuel; Blum, Christian: Beam-ACO for the travelling salesman problem with time windows (2010)
  12. Twomey, C.; Stützle, T.; Dorigo, M.; Manfrin, M.; Birattari, M.: An analysis of communication policies for homogeneous multi-colony ACO algorithms (2010) ioport
  13. Yu, Vincent F.; Lin, Shih-Wei; Chou, Shuo-Yan: The museum visitor routing problem (2010)
  14. Jourdan, L.; Basseur, M.; Talbi, E.-G.: Hybridizing exact methods and metaheuristics: a taxonomy (2009)
  15. Tamura, Naoyuki; Taga, Akiko; Kitagawa, Satoshi; Banbara, Mutsunori: Compiling finite linear CSP into SAT (2009)
  16. Andresen, Michael; Bräsel, Heidemarie; Mörig, Marc; Tusch, Jan; Werner, Frank; Willenius, Per: Simulated annealing and genetic algorithms for minimizing mean flow time in an open shop (2008)
  17. Bautista, Joaquín; Pereira, Jordi; Adenso-Díaz, Belarmino: A beam search approach for the optimization version of the car sequencing problem (2008)
  18. Blum, Christian: Beam-ACO for simple assembly line balancing (2008)
  19. Blum, Christian; Roli, Andrea: Hybrid metaheuristics: an introduction (2008)
  20. Huang, Kuo-Ling; Liao, Ching-Jong: Ant colony optimization combined with taboo search for the job shop scheduling problem (2008)

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