图 书 馆
差分进化算法和粒子群优化算法的聚类算法Differential Evolution and Particle Swarm Optimization in Partitional Clustering |
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| 课程网址: | http://videolectures.net/solomon_krink_depso/ |
| 主讲教师: | Thiemo Krink |
| 开课单位: | 奥胡斯大学 |
| 开课时间: | 2007-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In recent years, many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Surprisingly, very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms for numerical optimization, which are hardly known outside the heuristic search field, are particle swarm optimisation (PSO) and differential evolution (DE). In this study, we compared the performance of GAs with PSO and DE for a medoid evolution approach to clustering. Moreover, we compared these results with the nominal classification, k-means and random search (RS) as a lower bound. Our results show that DE is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both in respect to precision as well as robustness (reproducibility) of the results. Only for trivial problems all algorithms can obtain comparable results. Apart from superior performance, DE is very easy to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional cluster algorithms. |
| 关 键 词: | 聚类算法; 遗传算法; 粒子群优化; 差分进化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：wuyq |
| 阅读次数: | 61 |