- Tytuł:
- Cellular particle swarm optimization with a simple adaptive local search strategy for the permutation flow shop scheduling problem
- Autorzy:
-
Seck-Tuoh-Mora, Juan C.
Medina-Marin, Joselito
Martinez-Gomez, Erick S.
Hernandez-Gress, Eva S.
Hernandez-Romero, Norberto
Volpi-Leon, Valeria - Powiązania:
- https://bibliotekanauki.pl/articles/230060.pdf
- Data publikacji:
- 2019
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
flow shop
particle swarm optimization (PSO)
local search strategy
hybrid search method
cellular automata
scheduling - Opis:
- Permutation flow shop scheduling problem deals with the production planning of a number of jobs processed by a set of machines in the same order. Several metaheuristics have been proposed for minimizing the makespan of this problem. Taking as basis the previous Alternate Two-Phase PSO (ATPPSO) method and the neighborhood concepts of the Cellular PSO algorithm proposed for continuous problems, this paper proposes the improvement of ATPPSO with a simple adaptive local search strategy (called CAPSO-SALS) to enhance its performance. CAPSO-SALS keeps the simplicity of ATPPSO and boosts the local search based on a neighborhood for every solution. Neighbors are produced by interchanges or insertions of jobs which are selected by a linear roulette scheme depending of the makespan of the best personal positions. The performance of CAPSO-SALS is evaluated using the 12 different sets of Taillard’s benchmark problems and then is contrasted with the original and another previous enhancement of the ATPPSO algorithm. Finally, CAPSO-SALS is compared as well with other ten classic and state-of-art metaheuristics, obtaining satisfactory results.
- Źródło:
-
Archives of Control Sciences; 2019, 29, 2; 205-226
1230-2384 - Pojawia się w:
- Archives of Control Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł