A guided population archive whale optimization algorithm for solving multiobjective optimization problems

作者：

Highlights：

• Use an external archive to guide the whales swarm towards the optimal Pareto set.

• Adapt the whales movements to explore a multi-objective search space.

• Use the crowding distance to preserve the diversity of the whales swarm.

• The proposed algorithm provides a better spread of solutions with faster convergence.

摘要

•Use an external archive to guide the whales swarm towards the optimal Pareto set.•Adapt the whales movements to explore a multi-objective search space.•Use the crowding distance to preserve the diversity of the whales swarm.•The proposed algorithm provides a better spread of solutions with faster convergence.

论文关键词：Multiobjective optimization problems (MOPs),Pareto dominance,Whale optimization algorithm (WOA),Crowding distance

论文评审过程：Received 5 February 2019, Revised 17 August 2019, Accepted 20 September 2019, Available online 26 September 2019, Version of Record 3 October 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112972