A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization
作者：
Highlights：
• A new method has been proposed, known as MSCA, for global optimization.
• The MSCA improves the SCA using a novel transition parameter and mutation operator.
• A set of 33 benchmark problems is used to examine the MSCA.
• The MSCA is also used to solve real-engineering problems and to train multilayer perceptron.
• Comparisons illustrate the improvement in the performance of the MSCA.
摘要
•A new method has been proposed, known as MSCA, for global optimization.•The MSCA improves the SCA using a novel transition parameter and mutation operator.•A set of 33 benchmark problems is used to examine the MSCA.•The MSCA is also used to solve real-engineering problems and to train multilayer perceptron.•Comparisons illustrate the improvement in the performance of the MSCA.
论文关键词：Optimization,Sine Cosine Algorithm,Exploration and exploitation,Multilayer perceptron,Engineering optimization problems,Algorithm,Benchmark,Grey Wolf Optimizer,Particle Swarm Optimization,Genetic Algorithm
论文评审过程：Received 14 October 2019, Revised 15 January 2020, Accepted 17 March 2020, Available online 28 March 2020, Version of Record 24 April 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113395
