Solving artificial ant problem using two artificial bee colony programming versions

Boudardara F., GÖRKEMLİ B.

APPLIED INTELLIGENCE, cilt.50, sa.11, ss.3695-3717, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 11
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10489-020-01741-0
  • Dergi Adı: APPLIED INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), INSPEC, zbMATH, Library, Information Science & Technology Abstracts (LISTA)
  • Sayfa Sayıları: ss.3695-3717
  • Erciyes Üniversitesi Adresli: Evet

Özet

Artificialant problem is considered as a sub-problem of robotic path planning. In this study, it is solved using two different methods: artificial bee colony programming and a new version of it called shrinking artificial bee colony programming. The former is a novel evolutionary computation based automatic programming method based on artificial bee colony algorithm and it was previously applied to this problem by the researchers. However, in this study, more comprehensive analyses and comparison study are provided. The shrinking artificial bee colony programming was developed in this study and its basic idea is to reduce the number of food sources, periodically, instead of a constant number used in the artificial bee colony programming. First, some parameter tuning studies were carried out for the shrinking artificial bee colony programming. Then, performances of the artificial bee colony programming, shrinking artificial bee colony programming and some other evolutionary computation based automatic programming methods were compared on Santa Fe and Los Altos Hills trails. Simulation results and the comparison study show that both of the algorithms can be used to solve the artificial ant problem effectively. Furthermore, the periodically decreasing population size property added to the artificial bee colony programming improves the performance of the algorithm on the artificial ant problem. While the proposed approach shows one of the superior performances among the considered methods, the results of the artificial bee colony programming are competitive to the methods in the comparison study.