Clustering is a technique employed for data mining and analysis. k-means is one of the algorithms utilized for clustering. However, the answer derived using this algorithm is dependent on the initial solution and hence easily retrieves the optimal local answers. To overcome the disadvantages of this algorithm, in this paper a combination of pollination of flowers algorithm and genetic algorithm, named FPAGA, is presented. Combination algorithms are used to diversify the search space of the solution and to improve its capability. To elaborate, crossover and discarding of pollens operator are utilized to increase the population diversity, while elitism operator is employed to improve the local search capabilities. Five datasets are selected to evaluate the performance of the proposed algorithm. The evaluation results demonstrate not only greater accuracy but also better stability compared to the FPA, GA, FA, DE, and k-means algorithms. Moreover, faster convergence is evident, according to the obtained statistical results.

中文翻译：

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基于FPA和GA的混合进化算法分析聚类

聚类是用于数据挖掘和分析的技术。ķ-means是用于聚类的算法之一。但是，使用此算法得出的答案取决于初始解，因此很容易获得最佳的本地答案。为了克服该算法的缺点，提出了一种将花粉授粉算法与遗传算法相结合的方法，称为FPAGA。组合算法用于使解决方案的搜索空间多样化并提高其功能。详细说来，花粉运算符的交叉和丢弃被用来增加种群多样性，而精英运算符被用来提高局部搜索能力。选择五个数据集以评估所提出算法的性能。评估结果表明，与FPA，GA，FA，DE，k均值算法。而且，根据获得的统计结果，可以看到更快的收敛。