An incremental ant colony algorithm with local search for continuous optimization
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
ACO R is one of the most popular ant colony optimization algorithms for tackling continuous optimization problems. In this paper, we propose IACO R-LS, which is a variant of ACO R that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACO R-LS with Mtsls1 (IACO R- Mtsls1) is not only a significant improvement over ACO R, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACO R-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimization problems. Copyright 2011 ACM.
Açıklama
Assoc. Comput. Mach., Spec. Interest;Group Genet. Evol. Comput. (ACM SIGEVO)
13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 -- 12 July 2011 through 16 July 2011 -- Dublin -- 86136
13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 -- 12 July 2011 through 16 July 2011 -- Dublin -- 86136
Anahtar Kelimeler
Ant colony optimization, Automatic parameter tuning, Continuous optimization, Local search
Kaynak
Genetic and Evolutionary Computation Conference, GECCO'11
WoS Q Değeri
Scopus Q Değeri
N/A
