An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation
An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation
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基于膜系统和量子行为粒子群优化的扩展类组织 P 系统用于图像分割
DOI：
10.3390/pr10020287
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发表时间：
2022-01
期刊：
影响因子：
3.5
通讯作者：
Ning Wang
中科院分区：
文献类型：
--
作者：
Lin Wang;Xiyu Liu;Jianhua Qu;Yuzhen Zhao;Zhenni Jiang;Ning Wang
本文设计并开发了一种基于量子行为粒子群优化（QPSO）和改进的QPSO进化机制的扩展膜系统，该扩展膜系统使用具有进化同向/反向运输规则的类组织P系统和启动子/抑制剂，命名为CQPSO-ETP。 CQPSO-ETP 的目的是增强基于统计网络结构的膜启发进化算法（基于 SNS 的 MIEA）和 QPSO 技术的优化性能。在CQPSO-ETP中，引入基于标准QPSO机制的启动子进化规则来进化对象，并采用基于改进的QPSO机制的抑制剂进化规则，利用自适应选择以及协作进化和逻辑混沌映射方法来避免早熟。引入对象的启动子/抑制剂的通信规则，实现不同膜之间的信息交换和共享。在演化和通信机制的控制下，CQPSO-ETP借助分布式并行计算模型能够有效提升性能。将所提出的 CQPSO-ETP 与 PSO、QPSO 和两种现有的改进 QPSO 方法进行比较，并在八个经典数值基准函数上进行比较，以验证其有效性。此外，采用三种比较聚类方法对八张测试图像进行了计算实验，实验结果证明了所提出的CQPSO-ETP的聚类有效性。
An extended membrane system using a tissue-like P system with evolutional symport/antiport rules and a promoter/inhibitor, which is based on the evolutionary mechanism of quantum-behaved particle swarm optimization (QPSO) and improved QPSO, named CQPSO-ETP, is designed and developed in this paper. The purpose of CQPSO-ETP is to enhance the optimization performance of statistical network structure-based membrane-inspired evolutionary algorithms (SNS-based MIEAs) and the QPSO technique. In CQPSO-ETP, evolution rules with a promoter based on a standard QPSO mechanism are introduced to evolve objects, and evolution rules with an inhibitor based on an improved QPSO mechanism using self-adaptive selection, and cooperative evolutionary and logistic chaotic mapping methods, are adopted to avoid prematurity. The communication rules with a promoter/inhibitor for objects are introduced to achieve the exchange and sharing of information between different membranes. Under the control of the evolution and communication mechanism, the CQPSO-ETP can effectively improve the performance with the help of a distributed parallel computing model. The proposed CQPSO-ETP is compared with PSO, QPSO and two existing improved QPSO approaches which are conducted on eight classic numerical benchmark functions to verify the effectiveness. Furthermore, computational experiments which are made on eight tested images with three comparative clustering approaches are adopted, and the experimental results demonstrate the clustering validity of the proposed CQPSO-ETP.
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