An ant colony optimization algorithm with evolutionary experience-guided pheromone updating strategies for multi-objective optimization

作者：

Highlights：

• An intragroup evolutionary information-guided pheromone updating strategy.

• A learning automata-based adaptive pheromone updating strategy.

• Comparison experiments on benchmark traveling salesman problems (TSPs).

• The proposed algorithm is used in solving the degenerate primer design problem.

摘要

•An intragroup evolutionary information-guided pheromone updating strategy.•A learning automata-based adaptive pheromone updating strategy.•Comparison experiments on benchmark traveling salesman problems (TSPs).•The proposed algorithm is used in solving the degenerate primer design problem.

论文关键词：Ant colony optimization,Multi-objective optimization,Historical experience,Pheromone updating

论文评审过程：Received 19 April 2021, Revised 19 March 2022, Accepted 29 March 2022, Available online 6 April 2022, Version of Record 18 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117151