Optimal foraging algorithm with direction prediction and Gaussian oscillation for constrained optimization problems
作者：
Highlights：
• Optimal foraging algorithm with direction prediction & Gaussian oscillation is built.
• New algorithm includes two update methods: prediction model and random model.
• Prediction model mends exploitation ability by predicting the evolutionary direction.
• Random model mends exploration by exploring the space with Gaussian distribution.
• The superiority of algorithm is verified on benchmark set and engineering problems.
摘要
•Optimal foraging algorithm with direction prediction & Gaussian oscillation is built.•New algorithm includes two update methods: prediction model and random model.•Prediction model mends exploitation ability by predicting the evolutionary direction.•Random model mends exploration by exploring the space with Gaussian distribution.•The superiority of algorithm is verified on benchmark set and engineering problems.
论文关键词：Optimal foraging algorithm,Transition matrix,Differential evolution,Constrained problem,CEC2017
论文评审过程：Received 30 January 2020, Revised 11 September 2020, Accepted 31 May 2022, Available online 3 June 2022, Version of Record 7 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117735
