A new clustering membrane system using a complex chained P system (CCP) based on evolutionary mechanism is designed, developed, implemented, and tested. The purpose of CCP is to solve clustering problems. In CCP, two kinds of evolution rules in different chained membranes are used to enhance the global search ability. The first kind of evolution rules using traditional and modified particle swarm optimization (PSO) clustering techniques are used to evolve the objects. Another based on differential evolution (DE) is introduced to further improve the global search ability. The communication rules are adopted to accelerate the convergence and avoid prematurity. Under the control of evolution-communication mechanism, the CCP can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This proposed CCP is compared with four existing PSO clustering approaches on eight real-life datasets to verify the validity. The computational results on tested images also clearly show the effectiveness of CCP in solving image segmentation problems.

中文翻译：

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基于进化机制的复杂链式P系统图像分割。

设计，开发，实施和测试了一种基于进化机制的使用复杂链式P系统（CCP）的新型簇膜系统。CCP的目的是解决集群问题。在CCP中，两种在不同链状膜上的进化规则被用来增强全局搜索能力。使用传统和改进的粒子群优化（PSO）聚类技术的第一类进化规则用于进化对象。引入了另一种基于差分进化（DE）的方法，以进一步提高全局搜索能力。采用通信规则可以加快收敛速度​​，避免过早出现。在进化交流机制的控制下，CCP可以借助分布式并行计算模型有效地搜索最佳分区并提高聚类性能。将该提议的CCP与八个真实数据集上的四个现有PSO聚类方法进行比较，以验证其有效性。在测试图像上的计算结果也清楚地表明了CCP在解决图像分割问题方面的有效性。