A uniform family of tissue P systems with protein on cells solving 3-coloring in linear time

Christinal, Hepzibah A.; Díaz-Pernil, Daniel; Mathu, T.

doi:10.1007/s11047-016-9590-1

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…The network structure based MIEAs, including the statistical network structure (SNS) and dynamical network structure (DNS), are the second kind of EMC [67]. SNSbased MIEAs are presented by using tissue-like P systems or neural-like P systems with various network topologies [68].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Liu

et al. 2022

Processes

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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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Section: Introductionmentioning

confidence: 99%