A hybrid self-adaptive sine cosine algorithm with opposition based learning
作者：
Highlights：
• A new method to solve global optimization and engineering problems called m-SCA.
• The m-SCA improves the SCA using self-adaptation and opposition based learning.
• Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.
• The m-SCA is also tested on engineering optimization problems.
• Comparisons illustrate the improvement on the performance of m-SCA.
摘要
•A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based learning.•Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.•The m-SCA is also tested on engineering optimization problems.•Comparisons illustrate the improvement on the performance of m-SCA.
论文关键词：Population based algorithms,Sine Cosine algorithm (SCA),Opposition based learning,Self-adaptation,Benchmark test problems,Engineering application problems
论文评审过程：Received 5 July 2018, Revised 15 September 2018, Accepted 31 October 2018, Available online 1 November 2018, Version of Record 5 November 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.10.050
