Enhancing the food locations in an Artificial Bee Colony algorithm

Author

Sharma, Tarun Kumar ; Pant, Millie

Author_Institution

Dept. of Paper Technol., Indian Inst. of Technol., Roorkee, India

fYear

2011

fDate

11-15 April 2011

Firstpage

1

Lastpage

5

Abstract

Artificial Bee Colony or ABC is one of the newest additions to the class of population based Nature Inspired Algorithms (NIA). In the present study we suggest some modifications in the structure of basic ABC to further improve its performance. The corresponding algorithm proposed in the present study is named Intermediate ABC (I-ABC). In I-ABC, the potential food sources are generated by using the intermediate positions between the uniformly generated random numbers and random numbers generated by opposition based learning (OBL). The proposed I-ABC is further modified by guiding the bees towards the best food location. I-ABC is validated on a set of 15 benchmark problems with bound constraints. The numerical results indicate the competence of the proposed I-ABC algorithm.

Keywords

learning (artificial intelligence); number theory; optimisation; I-ABC algorithm; artificial bee colony algorithm; food locations; intermediate ABC; opposition based learning; population based nature inspired algorithms; population-based swarm intelligence algorithm; uniformly generated random numbers; Algorithm design and analysis; Artificial neural networks; Benchmark testing; Convergence; Equations; Mathematical model; Optimization; artificial bee colony; gbest; opposition based learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Swarm Intelligence (SIS), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-61284-053-6

Type

conf

DOI

10.1109/SIS.2011.5952582

Filename

5952582