An aggregative learning gravitational search algorithm with self-adaptive gravitational constants
作者：
Highlights：
• A new aggregative learning gravitational search algorithm is proposed.
• A self-adaptive gravitational constant is introduced into the algorithm.
• Extensive performance comparison with other state-of-the-art algorithms is done.
• Neural network learning tasks show the proposed algorithm’s practicability.
• The time complexity of the proposed algorithm is analyzed.
摘要
•A new aggregative learning gravitational search algorithm is proposed.•A self-adaptive gravitational constant is introduced into the algorithm.•Extensive performance comparison with other state-of-the-art algorithms is done.•Neural network learning tasks show the proposed algorithm’s practicability.•The time complexity of the proposed algorithm is analyzed.
论文关键词：Gravitational search algorithm,Gravitational constant,Elite individuals,Exploration and exploitation,Aggregative learning,Neural network learning
论文评审过程：Received 7 January 2020, Revised 13 March 2020, Accepted 17 March 2020, Available online 18 March 2020, Version of Record 3 April 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113396
