A dynamic stochastic search algorithm for high-dimensional optimization problems and its application to feature selection
作者：
Highlights：
• A novel metaheuristic algorithm called Dynamic Stochastic Search (DSS) is proposed.
• DSS is designed to solve high-dimensional optimization problems.
• DSS has a simple structure, low computational complexity, and less control parameters.
• Prove the effectiveness and feasibility of DSS using classical and CEC2014 benchmark functions.
• Prove the superiority of DSS on high-dimensional real-world optimization problems.
摘要
•A novel metaheuristic algorithm called Dynamic Stochastic Search (DSS) is proposed.•DSS is designed to solve high-dimensional optimization problems.•DSS has a simple structure, low computational complexity, and less control parameters.•Prove the effectiveness and feasibility of DSS using classical and CEC2014 benchmark functions.•Prove the superiority of DSS on high-dimensional real-world optimization problems.
论文关键词：Metaheuristic algorithm,Dynamic stochastic search,High-dimensional optimization problems,Feature selection
论文评审过程：Received 31 March 2021, Revised 4 January 2022, Accepted 26 February 2022, Available online 15 March 2022, Version of Record 26 March 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.108517
