Honey formation optimization framework for design problems

作者：

Highlights：

• Honey formation optimization (HFO) is proposed as a framework for artificial bee colony algorithms (ABC), which improves the effectiveness of current ABC variants.

• The work proposes new optimization algorithms: HFO-ABC and HFO-GABC with their performance tests on well-known 9 benchmark functions.

• Each food source has its own associated honey form which is conceptually decomposed into components by using solution decomposition or function decomposition.

• The results show that HFO framework increases the exploitation and exploration abilities of ABC variants significantly.

摘要

•Honey formation optimization (HFO) is proposed as a framework for artificial bee colony algorithms (ABC), which improves the effectiveness of current ABC variants.•The work proposes new optimization algorithms: HFO-ABC and HFO-GABC with their performance tests on well-known 9 benchmark functions.•Each food source has its own associated honey form which is conceptually decomposed into components by using solution decomposition or function decomposition.•The results show that HFO framework increases the exploitation and exploration abilities of ABC variants significantly.

论文关键词：Honey formation optimisation,Component design,Honey function,Bee colony algorithm,Objective function decomposition

论文评审过程：Received 21 February 2020, Revised 1 September 2020, Accepted 11 November 2020, Available online 27 November 2020, Version of Record 19 December 2020.

论文官网地址：https://doi.org/10.1016/j.amc.2020.125815