Homeostasis Tissue-Like P Systems

Timed Tissue P Systems With Channel States

Tissue P systems with channel states are a variant of tissue P systems that can be employed as highly parallel computing devices, where the channel states can control the movements of objects. In a sense, the time-free approach can improve the robustness of P systems; hence, in this work, we introduce the time-free property into such P systems and explore their computational performances. Specifically, in a time-free manner, it is proved that this type of P systems have Turing universality by using two cells and four channel states with a maximum rule length of 2, or by using two cells and noncooperative symport rules with a maximum rule length of 1. Moreover, in terms of computational efficiency, it is proved that a uniform solution of the satisfiability ( SAT ) problem can be obtained in a time-free manner by applying noncooperative symport rules with a maximum rule length of 1. The research results of this paper show that a highly robust dynamic membrane computing system is constructed. Theoretically, relative to the existing system, our constructed system can enhance robustness and expand its application scope.

An improved multi-view spectral clustering based on tissue-like P systems

Multi-view spectral clustering is one of the multi-view clustering methods widely studied by numerous scholars. The first step of multi-view spectral clustering is to construct the similarity matrix of each view. Consequently, the clustering performance will be greatly affected by the quality of the similarity matrix of each view. To solve this problem well, an improved multi-view spectral clustering based on tissue-like P systems is proposed in this paper. The optimal per-view similarity matrix is generated in an iterative manner. In addition, spectral clustering is combined with the symmetric nonnegative matrix factorization method to directly output the clustering results to avoid the secondary operation, such as k-means or spectral rotation. Furthermore, improved multi-view spectral clustering is integrated with the tissue-like P system to enhance the computational efficiency of the multi-view clustering algorithm. Extensive experiments verify the effectiveness of this algorithm over other state-of-the-art algorithms.

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

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.