A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical OptimizationReport as inadecuate

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Computational Intelligence and Neuroscience - Volume 2016 2016, Article ID 6097484, 17 pages -

Research Article

Communications Engineering, Chongqing University, Chongqing 400030, China

Jiuquan Satellite Launch Center, Jiuquan 732750, China

Received 26 January 2016; Revised 11 April 2016; Accepted 26 April 2016

Academic Editor: Christian W. Dawson

Copyright © 2016 Binglian Zhu 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.


This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position QMBA. In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution.

Author: Binglian Zhu, Wenyong Zhu, Zijuan Liu, Qingyan Duan, and Long Cao

Source: https://www.hindawi.com/


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