Universal enzymatic numerical P systems with small number of enzymatic variables

Zhang, Zhiqiang; Wu, Tingfang; Păun, Andrei; Pan, Linqiang

doi:10.1007/s11432-017-9103-5

Cited by 16 publications

(6 citation statements)

References 25 publications

(42 reference statements)

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“…Moreover, simulation softwares of NP systems and their applications have been investigated, for example, robot [39]- [44]. In addition, computing power of NP systems and variants has been discussed in the recent years [45]- [49].…”

Section: Remarkmentioning

confidence: 99%

Stochastic Numerical P Systems With Application in Data Clustering Problems

et al. 2020

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This paper proposes an extension of numerical P (NP) systems, called stochastic numerical P (StNP) systems. In StNP systems, a novel stochastic production function-repartition protocol is developed to evolve the variables. Compared with usual production function-repartition protocol in NP systems, there are two differences in stochastic production function-repartition protocol: (i) some stochastic/randomized factors are introduced in production function and/or repartition protocol; (ii) data updating way is relaxed and can be flexibly used. An StNP system has a nested structure and contains some variables and a group of programs. The programs consist of stochastic production function-repartition protocols and/or usual production function-repartition protocols. Therefore, StNP systems are a class of distributed parallel computing models with stochastic and dynamic characteristics. Data clustering problems are used as an application to demonstrate the availability and effectiveness of StNP systems. Based on StNP systems, a novel partition clustering algorithm is presented. Experimental results demonstrate advantage of StNP systems for data clustering problems.

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Section: Remarkmentioning

confidence: 99%

Stochastic Numerical P Systems With Application in Data Clustering Problems

et al. 2020

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“…Non-deterministic polynomial (NP) problems are the touchstone of intelligent algorithms [17]. Basic PIO has proven its worth in many aspects of practical NP issues ranging from energy system design [18] and home energy management [19] to protein complex detecting [20] and automatic disease detection [21].…”

Section: Variants Of Pigeon-inspired Optimizationmentioning

confidence: 99%

Advancements in pigeon-inspired optimization and its variants

Duan

Qiu

2019

Sci. China Inf. Sci.

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The returning of homing pigeons to their lofts from remote and unfamiliar locations with great accuracy remains a mystery. Pigeon-inspired optimization (PIO), which is a novel mono-objective continuous optimization algorithm, is inspired by the hidden mechanism behind the remarkable navigation capacity of homing pigeons. Since their development, PIO and its variants have been widely applied to various fields ranging from combinatorial optimization to multi-objective optimization in many areas, such as aerospace, medicine, and energy. This study aims to review the modifications of PIO from four aspects of improvement measures, namely, component replacement, operation addition, structure adjustment, and application expansion. It also summarizes the problems of existing research and plots the course of future effort.

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“…The rule can be executed at a time t only if ej,i>trueprefixmin{y1,i(t),y2,i(t),…ykl,i(t)}. From the definition of ENPS, it is clear that with enzymes-like variables, the system can control multiple production functions to run in parallel in the same membrane deterministically [54]. Hence, it can overcome the disadvantages of traditional NPS that only run one rule nondeterministically at a time in a membrane.…”

Section: MC and Enpsmentioning

confidence: 99%

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

et al. 2019

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Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

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Universal enzymatic numerical P systems with small number of enzymatic variables

Cited by 16 publications

References 25 publications

Stochastic Numerical P Systems With Application in Data Clustering Problems

Stochastic Numerical P Systems With Application in Data Clustering Problems

Advancements in pigeon-inspired optimization and its variants

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

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