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中文摘要：
为了改善量子行为粒子群优化算法的收敛性能，避免粒子早熟问题，提出了一种基于完全学习策略的量子行为粒子群优化算法。由此设计了一种新的数据聚类算法，新的聚类算法通过特殊的粒子编码方式在聚类过程中能够自动确定最佳的聚类数目。在五个测试数据集上与其他两种动态聚类算法进行聚类实验比较，实验结果表明，基于完全学习策略的量子行为粒子群优化动态聚类算法能够获得较好的聚类结果，有着良好的应用前景。
英文摘要：
This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence, based on which introduced a novel data clustering algorithm as well. The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used. Compared with another two dynamic clustering algorithms on five testing data sets, the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best performance and with the best potential application prospect.
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A Quantum-behaved Particle Swarm Optimization with Diversity-guided Mutation for Design of Two-Dimen
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