Biological survival optimization algorithm with its engineering and neural network applications

Date

2023-02-13

Advisors

Journal Title

Journal ISSN

ISSN

1433-7479

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

Abstract

This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link

Keywords

Biological survival optimizer, Engineering structural problem, Neural network, Escape behavior

Citation

Wang, L., Zhang, Q., He, X., Yang, S., Jiang, S. and Dong, Y. (2023) Biological survival optimization algorithm with its engineering and neural network applications. Soft Computing, 27, pp. 6437–6463

Rights

Research Institute

Institute of Artificial Intelligence (IAI)