Biogeography-based optimisation with chaos
MetadataShow full item record
The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.
Neural Computing And Applications
© 2014 The Natural Computing Applications Forum. Published by Springer London. This is an electronic version of an article published in Neural Computing and Applications, Volume 25, Issue 5, pp 1077–1097, 2014. Neural Computing and Applications is available online at: http://link.springer.com/ with the open URL of your article.
Analysis of Algorithms and Complexity