ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment

作者：

Highlights：

• Sine cosine algorithm is enhanced by hybridizing particle swarm optimization.

• The hybridization is based on low level co-evolutionary hybrid scheme.

• The proposed scheme produced better performance on mathematical benchmark functions.

• Finding longest consecutive substring problem was used as testing case study.

摘要

•Sine cosine algorithm is enhanced by hybridizing particle swarm optimization.•The hybridization is based on low level co-evolutionary hybrid scheme.•The proposed scheme produced better performance on mathematical benchmark functions.•Finding longest consecutive substring problem was used as testing case study.

论文关键词：Sine cosine algorithm (SCA),Particle swarm optimization (PSO),Meta-heuristics algorithms,Pairwise local alignment,Longest consecutive substrings,Smith-Waterman alignment algorithm

论文评审过程：Received 16 September 2017, Revised 12 January 2018, Accepted 13 January 2018, Available online 3 February 2018, Version of Record 3 February 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.019