An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang, Lin; Liu, Xiyu; Sun, Minghe; Qu, Jianhua

doi:10.1155/2020/5097589

Cited by 5 publications

(4 citation statements)

References 53 publications

(54 reference statements)

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“…The paper also includes comparisons with other algorithms. In [32], the authors introduced a clustering membrane system, named PSO-CP. It uses a cell-like P system with active membranes based on particle swarm optimization.…”

Section: Membrane Clusteringmentioning

confidence: 99%

P System–Based Clustering Methods Using NoSQL Databases

2021

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Models of computation are fundamental notions in computer science; consequently, they have been the subject of countless research papers, with numerous novel models proposed even in recent years. Amongst a multitude of different approaches, many of these methods draw inspiration from the biological processes observed in nature. P systems, or membrane systems, make an analogy between the communication in computing and the flow of information that can be perceived in living organisms. These systems serve as a basis for various concepts, ranging from the fields of computational economics and robotics to the techniques of data clustering. In this paper, such utilization of these systems—membrane system–based clustering—is taken into focus. Considering the growing number of data stored worldwide, more and more data have to be handled by clustering algorithms too. To solve this issue, bringing these methods closer to the data, their main element provides several benefits. Database systems equip their users with, for instance, well-integrated security features and more direct control over the data itself. Our goal is if the type of the database management system is given, e.g., NoSQL, but the corporation or the research team can choose which specific database management system is used, then we give a perspective, how the algorithms written like this behave in such an environment, so that, based on this, a more substantiated decision can be made, meaning which database management system should be connected to the system. For this purpose, we discover the possibilities of a clustering algorithm based on P systems when used alongside NoSQL database systems, that are designed to manage big data. Variants over two competing databases, MongoDB and Redis, are evaluated and compared to identify the advantages and limitations of using such a solution in these systems.

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Section: Membrane Clusteringmentioning

confidence: 99%

P System–Based Clustering Methods Using NoSQL Databases

2021

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“…Specially, MIEAs or MAs are outstanding instances in MC for approaching real-word applications [45], which can be classified into two categories according to different membrane structure: hierarchical membrane structure from celllike P systems and network membrane structure from tissue-like P systems and neurallike P systems [46]. Furthermore, hierarchical structure based MIEAs, including a nested membrane structure (NMS) [47], one level membrane structure (OLMS) [48], hybrid membrane structure (HMS) [49] and dynamic membrane structure (DMS) [50], are the first kind of EMC. NMS-based MIEAs are designed to integrate cell-like P systems with different metaheuristic techniques, such as GA [51], DE [52,53], ACO [54], and so on [55,56].…”

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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“…Cell-like P systems, as a class computing model of MC, are inspired by the hierarchical structure and communication mechanism of living cells. The underlying membrane structure of cell-like P systems can be abstracted to arbitrary graphs in mathematics [4]. Cell-like P systems have highly effective efficiency in solving optimization problems with linear or polynomial complexity due to the distributed parallel computing working manner.…”

Section: Introductionmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

Mathematics

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An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

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An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Cited by 5 publications

References 53 publications

P System–Based Clustering Methods Using NoSQL Databases

P System–Based Clustering Methods Using NoSQL Databases

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

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

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