Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble

Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble
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基于邻域集成分解的自组织多目标优化

DOI:
10.1016/j.neucom.2015.08.092
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发表时间:
2016-01
期刊:
影响因子:
6
通讯作者:
Song Shenmin
Song Shenmin
中科院分区:
计算机科学2区
文献类型:
--
作者:
Zhang Hu;Zhang Xiujie;Gao Xiao-Zhi;Song Shenmin

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目前,大多数多目标进化算法(MOEAs)直接采用为单目标优化设计的再生算子。由于这些算子没有考虑多目标优化问题(MOPs)的特点,他们不能总是表现良好的MOEA。受此启发,本文提出了一种基于MOPs正则性的自组织再生机制,并提出了一种基于邻域集成分解的自组织多目标进化算法。在新的再生算法中,首先利用自组织映射方法发现种群分布结构,并为每个解建立交配池。此后,仅允许在相同交配池内的溶液之间进行复制。为了建立交配池,还引入了多个神经元邻域大小的集合。选择不同邻域大小的概率基于它们在过去某些代中产生新解的性能进行更新。综合实验表明,该算法是有效的和竞争力。新的再生机制和邻域系综的贡献也进行了实验验证。
Currently, most of the multiobjective evolutionary algorithms (MOEAs) directly adopt the reproduction operators designed for the single-objective optimization. Since these operators do not consider the characteristics of multiobjective optimization problems (MOPs), they cannot always perform well in the MOEAs. Inspired by this case, this paper presents a self-organizing reproduction mechanism based on the regularity property of MOPs, and proposes a self-organizing multiobjective evolutionary algorithm based on decomposition with neighborhood ensemble. In the new reproduction, a self-organizing map approach is firstly employed to discover the population distribution structure, and to build a mating pool for each solution. Thereafter, reproductions are only allowed among the solutions within the same mating pools. In order to establish the mating pools, an ensemble of multiple neuron neighborhood sizes is also introduced. The probability of choosing different neighborhood sizes is updated based on their performance on producing new solutions over the last certain generations. Comprehensive experiments denote that the proposed algorithm is efficient and competitive. The contributions of the new reproduction mechanism and neighborhood ensemble are also experimentally validated.
DOI: --
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期刊: J. Multiple Valued Log. Soft Comput.
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