@inproceedings{f1e0dea38a424cae9f0fe90ab1e4575c,
title = "Opposition-based Artificial Bee Colony algorithm",
abstract = "The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed opposition-based ABC (OABC) is compared to the performance of ABC and opposition-based Differential Evolution (ODE) when applied to the Black-Box Optimization Benchmarking (BBOB) library introduced in the previous two GECCO conferences.",
keywords = "Benchmarking, Black-box optimization, Opposition-based learning, Swarm intelligence",
author = "Mohammed El-Abd",
year = "2011",
doi = "10.1145/2001576.2001592",
language = "English",
isbn = "9781450305570",
series = "Genetic and Evolutionary Computation Conference, GECCO'11",
pages = "109--115",
booktitle = "Genetic and Evolutionary Computation Conference, GECCO'11",
note = "13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 ; Conference date: 12-07-2011 Through 16-07-2011",
}