A Many-objective Algorithm based on Staged Coordination Selection Juan Zou a,b , Jing Liu a,b,∗ , Jinhua Zheng a,b,c , Shengxiang Yang a,d,∗ a Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China b Faculty of Informational Engineering University of Xiangtan University, Xiangtan, 411105, China c Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China d School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH,U.K. Abstract Convergence and diversity are two performance requirements that should be paid attention to in evolutionary algorithms. Most multiobjective evolu- tionary algorithms (MOEAs) try their best to maintain a balance between the two aspects, which poses a challenge to the convergence of MOEAs in the early evolutionary process. In this paper, a many-objective optimiza- tion algorithm based on staged coordination selection, which consists of the convergence and diversity stages, is proposed in which the two stages are con- sidered separately in each iteration. In the convergence exploring stage, the decomposition method is adopted to rapidly make the population close to the true PF. In the diversity exploring stage, a diversity maintenance mechanism same to the archive truncation method of SPEA2 is used to push distribut- ed individuals to the true PF. The convergence stage serves for the diversity stage, and the second stage turns into the first stage when it fails to reach the convergence requirement and so forth. Our algorithm is compared with eight state-of-the-art many-objective optimization algorithms on DTLZ, WFG and MaOP benchmark instances. Results show that our algorithm outperformed * Corresponding author: Jing Liu, Shengxiang Yang Email addresses: liujinghn@qq.com (Jing Liu), syang@dmu.ac.uk (Shengxiang Yang) Preprint submitted to Swarm and Evolutionary Computation June 23, 2020