An Improved Bird Swarm Algorithm with Adaptive Characteristics

DOI

10.2991/cecs-18.2018.40How to use a DOI?

Keywords

bird swarm algorithm, inertial weight, disturbance strategy, simulation test.

Abstract

Bird swarm algorithm (BSA) is a new heuristic intelligent algorithm, which has been successfully applied in many fields. In view of the shortcomings of bird swarm algorithm which is easy to fall into local optimum and premature convergence. I propose an improved bird swarm algorithm (IBSA). Firstly, the initial population is constructed by chaos optimization algorithm, so that the initial solution is uniformly distributed in the solution space, thus improving the diversity of the population. Secondly, by introducing inertial weights, nonlinear adjustment of cognitive and social coefficients can be trade-off bird local and global search ability. Finally, a disturbance strategy is added to the forging position of the birds. Thus, the diversity of population in the late iteration period is enhanced, and the ability to jump out of the local optimum is improved. Through the simulation experiments of several benchmark functions and compared with other intelligent algorithms, the results show that the improved bird swarm algorithm (IBSA) has better convergence speed and optimization precision, which proves its superiority.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Chao Zhou
AU  - Lei Mei
AU  - Ryad Chellali
AU  - Yongkun Zhao
PY  - 2018/07
DA  - 2018/07
TI  - An Improved Bird Swarm Algorithm with Adaptive Characteristics
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 230
EP  - 235
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.40
DO  - 10.2991/cecs-18.2018.40
ID  - Zhou2018/07
ER  -