New fruit fly optimization algorithm with joint search strategies for function optimization problems

作者：

Highlights：

• A new parameter and new move direction guided by biological memory are used for search in proposed JS-FOA.

• Using a formula similar to the gradient descent in mathematical to escape from the local extreme.

• Comparative experiment containing 29 benchmark functions is tested to verify the performance of JS-FOA.

• Perform sensitivity analysis experiments on the two parameters of JS-FOA to verify the stability of the algorithm.

摘要

•A new parameter and new move direction guided by biological memory are used for search in proposed JS-FOA.•Using a formula similar to the gradient descent in mathematical to escape from the local extreme.•Comparative experiment containing 29 benchmark functions is tested to verify the performance of JS-FOA.•Perform sensitivity analysis experiments on the two parameters of JS-FOA to verify the stability of the algorithm.

论文关键词：Fruit fly optimization algorithm,Group collaboration,Memory move direction,Gradient descent search

论文评审过程：Received 21 July 2018, Revised 16 February 2019, Accepted 24 March 2019, Available online 9 April 2019, Version of Record 7 May 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.03.028