An improved multi-objective population-based extremal optimization algorithm with polynomial mutation
An improved multi-objective population-based extremal optimization algorithm with polynomial mutation
复制标题
一种改进的基于多项式变异的多目标群体极值优化算法
DOI：
10.1016/j.ins.2015.10.010
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发表时间：
2016-02
影响因子：
8.1
通讯作者：
Chong-Wei Zheng
中科院分区：
文献类型：
--
作者：
Min-Rong Chen;Lie Wu;Yu-Xing Dai;Chong-Wei Zheng
极值优化（EO）作为一种受自组织临界性的远离平衡态动力学启发而发展起来的进化算法，已成功地应用于各种基准和工程优化问题。然而，关于进化优化在多目标优化领域中的应用研究却很少。提出了一种改进的基于种群的多目标进化算法IMOPEO-PLM，用于求解多目标优化问题。与以前的多目标版本的基础上EO，建议IMOPEO-PLM采用基于人口的迭代优化，更有效的变异操作称为多项式变异，和一个新的，更有效的机制，产生新的人口。从多目标进化算法（MOEAs）的设计角度来看，IMOPEO-PLM由于其可调参数少且只进行变异操作，相对于其他已报道的竞争MOEAs而言相对简单。此外，通过使用非参数统计检验，在一些基准MOP上的大量实验结果表明，IMOPEO-PLM的性能优于或至少与这些报道的流行MOEA竞争，例如MOPEO，MOEO，NSGA-II，A-MOCLPSO，PAES，SPEA，SPEA 2，SMS-EMOA，SMPSO和MOEA/D-DE，例如，Kruskal-Wallis检验、Mann-WhitneyUtest、Friedman和Quade检验，针对一些常用的定量性能指标，例如，收敛，多样性（扩散），超容量，代际距离，反向代际距离。
As a recently developed evolutionary algorithm inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of benchmark and engineering optimization problems. However, there are only few reported research works concerning the applications of EO in the field of multi-objective optimization. This paper presents an improved multi-objective population-based EO algorithm with polynomial mutation called IMOPEO-PLM to solve multi-objective optimization problems (MOPs). Unlike the previous multi-objective versions based on EO, the proposed IMOPEO-PLM adopts population-based iterated optimization, a more effective mutation operation called polynomial mutation, and a novel and more effective mechanism of generating new population. From the design perspective of multi-objective evolutionary algorithms (MOEAs), IMOPEO-PLM is relatively simpler than other reported competitive MOEAs due to its fewer adjustable parameters and only mutation operation. Furthermore, the extensive experimental results on some benchmark MOPs show that IMOPEO-PLM performs better than or at least competitive with these reported popular MOEAs, such as MOPEO, MOEO, NSGA-II, A-MOCLPSO, PAES, SPEA, SPEA2, SMS-EMOA, SMPSO, and MOEA/D-DE, by using nonparametric statistical tests, e.g., Kruskal–Wallis test, Mann–WhitneyUtest, Friedman and Quade tests, in terms of some commonly-used quantitative performance metrics, e.g., convergence, diversity (spread), hypervolume, generational distance, inverted generational distance.
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DOI：
10.1016/j.ins.2014.07.020
发表时间：
2014-12
期刊：
Inf. Sci.
影响因子：
--
作者：
Siwei Jiang;Jie Zhang;Y. Ong
通讯作者：
Siwei Jiang;Jie Zhang;Y. Ong
DOI：
10.1016/j.ejor.2007.05.008
发表时间：
2008-08
期刊：
Eur. J. Oper. Res.
影响因子：
--
作者：
Min-Rong Chen;Yongzai Lu
通讯作者：
Min-Rong Chen;Yongzai Lu
DOI：
10.1109/ic3ina.2013.6819162
发表时间：
2013-11
期刊：
2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)
影响因子：
--
作者：
S. Khan
通讯作者：
S. Khan
一种基于分解的外部档案引导组合优化多目标进化算法
影响因子：
14.3
作者：
Cai, Xinye;Li, Yexing;Zhang, Qingfu
通讯作者：
Zhang, Qingfu
DOI：
--
发表时间：
1991
期刊：
--
影响因子：
--
作者：
C. Janikow;Z. Michalewicz
通讯作者：
C. Janikow;Z. Michalewicz