Local preference-inspired co-evolutionary algorithms
Local preference-inspired co-evolutionary algorithms
复制标题
DOI：
10.1145/2330163.2330236
复制
发表时间：
2012-07
期刊：
影响因子：
--
通讯作者：
Rui Wang;R. Purshouse;P. Fleming
中科院分区：
文献类型：
--
作者：
Rui Wang;R. Purshouse;P. Fleming
偏好启发的协同进化算法（picea）是一类新的方法，已被证明在多目标问题（MOPs）上表现良好。picea的良好性能在很大程度上归功于其巧妙的竞争协同进化的适应度计算方法。然而，这种适应度计算方法存在潜在的局限性。在这项工作中，我们分析了这一限制，并提出在局部结构（LPICEAs）中实现PICEAs。通过使用本地结构，本地操作的好处被纳入picea。同时，解决了原有适应度计算方法的局限性。首先根据聚类技术将候选解划分为若干类；然后在每个集群上分别执行进化操作，即生存选择和遗传变异。为了验证lpicea的性能，将lpicea与picea在一些基准函数上进行比较。实验结果表明，在大多数基准测试中，lpicea的性能明显优于picea。此外，还研究了LPICEAs对参数k（即LPICEAs中使用的簇数）调优的影响。结果表明，lpicea的性能对参数k非常敏感。
Preference-inspired co-evolutionary algorithms (PICEAs) are a new class of approaches which have been demonstrated to perform well on multi-objective problems (MOPs). The good performance of PICEAs is largely due to its clever fitness calculation method which is in a competitive co-evolutionary way. However, this fitness calculation method has a potential limitation. In this work, we analyze this limitation and propose to implement PICEAs within a local structure (LPICEAs). By using the local structure, the benefits of local operations are incorporated into PICEAs. Meanwhile, the limitation of the original fitness calculation method is solved. In details, the candidate solutions are firstly partitioned into several clusters according to a clustering technique. Then the evolutionary operations, i.e. selection-for-survival and genetic-variation are executed on each cluster, separately. To validate the performance of LPICEAs, LPICEAs are compared to PICEAs on some benchmarks functions. Experimental results indicate LPICEAs significantly outperform PICEAs on most of the benchmarks. Moreover, the influence of LPICEAs to the tuning of the parameter k, i.e. the number of clusters used in LPICEAs is studied. The results indicate that the performance of LPICEAs is sensitive to the parameter k.