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A many-objective algorithm based on staged coordination selection
Swarm and Evolutionary Computation ( IF 10 ) Pub Date : 2020-08-16 , DOI: 10.1016/j.swevo.2020.100737
Juan Zou , Jing Liu , Jinhua Zheng , Shengxiang Yang

Convergence and diversity are two performance requirements that should be paid attention to in evolutionary algorithms. Most multiobjective evolutionary 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 optimization algorithm based on staged coordination selection, which consists of the convergence and diversity stages, is proposed in which the two stages are considered 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 distributed 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 the comparison algorithms for most test problems.



中文翻译:

基于阶段协调选择的多目标算法

收敛性和多样性是进化算法中应注意的两个性能要求。大多数多目标进化算法(MOEA)尽力保持这两个方面之间的平衡,这对早期进化过程中MOEA的收敛提出了挑战。本文提出了一种基于阶段协调选择的多目标优化算法,该算法由收敛阶段和分集阶段组成,在每次迭代中分别考虑两个阶段。在收敛探索阶段,采用分解方法使种群迅速接近真实PF。在多样性探索阶段,使用与SPEA2的存档截断方法相同的多样性维护机制将分散的个人推向真正的PF。收敛阶段用于多样性阶段,第二阶段无法达到收敛要求时,第二阶段变为第一阶段。我们的算法与DTLZ,WFG和MaOP基准实例上的八种最新的多目标优化算法进行了比较。结果表明,对于大多数测试问题,我们的算法均优于比较算法。

更新日期:2020-08-16
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