A new bat algorithm based on iterative local search and stochastic inertia weight

作者：

Highlights：

• A new bat algorithm based on iterative local search and stochastic inertial weight (ILSSIWBA) is proposed.

• The global convergence of ILSSIWBA is proved by the convergence criteria of stochastic algorithm.

• ILSSIWBA has remarkable advantages in optimization accuracy, solving speed and convergence stability.

摘要

•A new bat algorithm based on iterative local search and stochastic inertial weight (ILSSIWBA) is proposed.•The global convergence of ILSSIWBA is proved by the convergence criteria of stochastic algorithm.•ILSSIWBA has remarkable advantages in optimization accuracy, solving speed and convergence stability.

论文关键词：Bat algorithm,Iterated local search,Stochastic inertia weight,Swarm intelligence,Global optimum

论文评审过程：Received 17 November 2017, Revised 12 February 2018, Accepted 10 March 2018, Available online 16 March 2018, Version of Record 30 March 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.03.015