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Home > Archive>Volume 31, Issue 2, 2020 >356-373. DOI:10.13328/j.cnki.jos.005617
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Many-objective Evolutionary Algorithm Based on Decomposition and Coevolution
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TP181

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National Natural Science Foundation of China (61763010, 61402481, 61165004); "BAGUI Scholar" Program of Guangxi Zhuang Autonomous Region of China; Hebei Youth Top Talent Support Program (冀字[2013]17); Natural Science Foundation of Hebei Province(F2015403046); Major Scientific Research Program of Education Bureau of Hebei Province (DZ2018083); Science and Technology Project of Education Bureau of Hubei Province (B2015240); Major Scientific Research Fund of Jingchu University of Technology (ZR201402); Startup Fund for Talents of Jingchu University of Technology (QDB201605)

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    Abstract:

    In real-world, there exist lots of many-objective optimization problems (MaOPs), which severely challenge well-known multi-objective evolutioanry algorithms (MOEAs). A many-obective evolutioanry algorithm combining decomposition and coevolution (MaOEA/DCE) is presented in this paper. MaOEA/DCE adopts mix-level orthogonal experimental design to produce a set of weight vectors evenly distributed in weight coefficient space, so as to improve the diversity of initial population. In addition, the MaOEA/DCE integrates differential evolution (DE) with the adaptive SBX operator to generate high-quality offspring for enhancing the convergence of evolutionary population. Some comparative experiments are conducted among MaOEA/DCE and other five representative MOEAs to examine their IGD+ performance on four MaOPs of DTLZ{1,2,4,5}. The experimental results show that the proposed MaOEA/DCE has overall performance advantage over the other peering MOEAs in terms of convergence, diversity, and robustness.

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谢承旺,余伟伟,闭应洲,汪慎文,胡玉荣.一种基于分解和协同的高维多目标进化算法.软件学报,2020,31(2):356-373

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History
  • Received:April 01,2018
  • Revised:May 25,2018
  • Online: February 17,2020
  • Published: February 06,2020
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