Multi-strategy boosted marine predators algorithm for optimizing approximate developable surface

作者：

Highlights：

• An enhanced marine predators algorithm termed NMPA is proposed.

• The proposed NMPA is tested on CEC2017 and CEC2020 test suites as well as 3 engineering design problems.

• The superiority and validity of NMPA is verified by comparing with multiple intelligent algorithms.

• Optimization model for constructing developable surface through two given space curves is established.

• The NMPA is applied to the solutions of the established optimization model.

摘要

•An enhanced marine predators algorithm termed NMPA is proposed.•The proposed NMPA is tested on CEC2017 and CEC2020 test suites as well as 3 engineering design problems.•The superiority and validity of NMPA is verified by comparing with multiple intelligent algorithms.•Optimization model for constructing developable surface through two given space curves is established.•The NMPA is applied to the solutions of the established optimization model.

论文关键词：Enhanced marine predators algorithm,Neighborhood learning strategy,Adaptive population size,Approximate developable surfaces,Developability degree

论文评审过程：Received 16 February 2022, Revised 1 August 2022, Accepted 3 August 2022, Available online 18 August 2022, Version of Record 30 August 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109615