Document Type

Article

Publication Date

1-1-2021

Publication Title

Comput Intell Neurosci

Keywords

JGM

JAX Source

Comput Intell Neurosci 2021 May 28; 2021:8056225

Volume

2021

First Page

8056225

Last Page

8056225

ISSN

1687-5273

PMID

34135953

DOI

https://doi.org/10.1093/neuonc/noab146

Abstract

Software testing is a widespread validation means of software quality assurance in industry. Intelligent optimization algorithms have been proved to be an effective way of automatic test data generation. Firefly algorithm has received extensive attention and been widely used to solve optimization problems because of less parameters and simple implement. To overcome slow convergence rate and low accuracy of the firefly algorithm, a novel firefly algorithm with deep learning is proposed to generate structural test data. Initially, the population is divided into male subgroup and female subgroup. Following the randomly attracted model, each male firefly will be attracted by another randomly selected female firefly to focus on global search in whole space. Each female firefly implements local search under the leadership of the general center firefly, constructed based on historical experience with deep learning. At the final period of searching, chaos search is conducted near the best firefly to improve search accuracy. Simulation results show that the proposed algorithm can achieve better performance in terms of success coverage rate, coverage time, and diversity of solutions.

Comments

This article is licensed under a Creative Commons Attribution 4.0 International License.

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