Adaptive neighborhood selection for many-objective optimization problems
Adaptive neighborhood selection for many-objective optimization problems
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DOI：
10.1016/j.asoc.2017.11.041
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发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Juan Zou;Yuping Zhang;Shengxiang Yang;Yuan Liu;Jinhua Zheng
中科院分区：
文献类型：
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作者：
Juan Zou;Yuping Zhang;Shengxiang Yang;Yuan Liu;Jinhua Zheng
人们普遍认为，随着目标数量的增加，收敛和分布之间的冲突会恶化。此外，帕累托优势失去了它的有效性在多目标优化问题（MaOP），其中有三个以上的目标。因此，需要一种更有效的选择方法来平衡收敛和分布。本文提出了一种多目标进化算法--自适应邻域选择多目标进化算法（ANS-MOEA）。该方法通过两种类型的信息来定义每个个体的性能，即收敛信息（CI）和分布信息（DI）。在临界层中，首先从种群中选择一个收敛良好的个体，然后将其邻居（由DI计算）推入邻居集合（NC）。然后，通过竞争确保种群的适当分布，具有大DI的个体返回到种群，具有小DI的个体留在集合中。四个国家的最先进的MaOEAs被选为竞争力的算法来验证ANS-MOEA。实验结果表明，ANS-MOEA可以解决一个MaOP，并产生一组显着的解决方案，以平衡收敛和分布。
It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization problems (MaOPs), which have more than three objectives. Therefore, a more valid selection method is needed to balance convergence and distribution. This paper presents a many-objective evolutionary algorithm, calledAdaptive Neighborhood Selection for Many-objective evolutionary algorithm(ANS-MOEA), to deal with MaOPs. This method defines the performance of each individual by two types of information, convergence information (CI) and distribution information (DI). In the critical layer, a well-converged individual is selected first from the population, and its neighbors, calculated by DI, are pushed into neighbor collection (NC) soon afterwards. Then, the proper distribution of the population is ensured by competition individuals with large DI go back to the population and individuals with small DI remain in the collection. Four state-of-the-art MaOEAs are selected as the competitive algorithms to validate ANS-MOEA. The experimental results show that ANS-MOEA can solve a MaOP and generate a set of remarkable solutions to balance convergence and distribution.