Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood SearchReportar como inadecuado

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Computational Intelligence and Neuroscience - Volume 2017 2017, Article ID 3235720, 15 pages - https:-doi.org-10.1155-2017-3235720

Research Article

National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China

University of Chinese Academy of Sciences, Beijing 100049, China

Correspondence should be addressed to Rui Han

Received 27 February 2017; Accepted 20 April 2017; Published 28 May 2017

Academic Editor: Michael Schmuker

Copyright © 2017 Xingwang Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Binary bat algorithm BBA is a binary version of the bat algorithm BA. It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm IBBA to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization BPSO. Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

Autor: Xingwang Huang, Xuewen Zeng, and Rui Han

Fuente: https://www.hindawi.com/


Documentos relacionados