Improving artificial algae algorithm performance by predicting candidate solution quality
作者：
Highlights：
• The proposed method can find better results with same maximum fitness evaluations.
• Candidate solution prediction boosted the performance of artificial algae algorithm.
• The proposed method has shown better convergence rate than 7competitor algorithms.
• The new method contributed for better balance of exploration and exploitation.
• Probabilistic model helped to achieve better result with fewer function evaluations.
摘要
•The proposed method can find better results with same maximum fitness evaluations.•Candidate solution prediction boosted the performance of artificial algae algorithm.•The proposed method has shown better convergence rate than 7competitor algorithms.•The new method contributed for better balance of exploration and exploitation.•Probabilistic model helped to achieve better result with fewer function evaluations.
论文关键词：Swarm intelligence,Artificial algae algorithm,Naïve Bayes,Candidate solution prediction
论文评审过程：Received 23 February 2019, Revised 5 February 2020, Accepted 6 February 2020, Available online 13 February 2020, Version of Record 20 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113298
