scholarly journals On Distributed Solution to SAT by Membrane Computing

2018 ◽  
Vol 13 (3) ◽  
pp. 303-320 ◽  
Author(s):  
Henry N. Adorna ◽  
Linqiang Pan ◽  
Bosheng Song

Tissue P systems with evolutional communication rules and cell division (TPec, for short) are a class of bio-inspired parallel computational models, which can solve NP-complete problems in a feasible time. In this work, a variant of TPec, called $k$-distributed tissue P systems with evolutional communication and cell division ($k\text{-}\Delta_{TP_{ec}}$, for short) is proposed. A uniform solution to the SAT problem by $k\text{-}\Delta_{TP_{ec}}$ under balanced fixed-partition is presented. The solution provides not only the precise satisfying truth assignments for all Boolean formulas, but also a precise amount of possible such satisfying truth assignments. It is shown that the communication resource for one-way and two-way uniform $k$-P protocols are increased with respect to $k$; while a single communication is shown to be possible for bi-directional uniform $k$-P protocols for any $k$. We further show that if the number of clauses is at least equal to the square of the number of variables of the given boolean formula, then $k\text{-}\Delta_{TP_{ec}}$ for solving the SAT problem are more efficient than TPec as show in \cite{bosheng2017}; if the number of clauses is equal to the number of variables, then $k\text{-}\Delta_{TP_{ec}}$ for solving the SAT problem work no much faster than TPec.

2006 ◽  
Vol 17 (01) ◽  
pp. 127-146 ◽  
Author(s):  
ALBERTO LEPORATI ◽  
CLAUDIO ZANDRON ◽  
MIGUEL A. GUTIÉRREZ-NARANJO

Current P systems which solve NP–complete numerical problems represent the instances of the problems in unary notation. However, in classical complexity theory, based upon Turing machines, switching from binary to unary encoded instances generally corresponds to simplify the problem. In this paper we show that, when working with P systems, we can assume without loss of generality that instances are expressed in binary notation. More precisely, we propose a simple method to encode binary numbers using multisets, and a family of P systems which transforms such multisets into the usual unary notation. Such a family could thus be composed with the unary P systems currently proposed in the literature to obtain (uniform) families of P systems which solve NP–complete numerical problems with instances encoded in binary notation. We introduce also a framework which can be used to design uniform families of P systems which solve NP–complete problems (both numerical and non-numerical) working directly on binary encoded instances, i.e., without first transforming them to unary notation. We illustrate our framework by designing a family of P systems which solves the 3-SAT problem. Next, we discuss the modifications needed to obtain a family of P systems which solves the PARTITION numerical problem.


2015 ◽  
Vol 27 (1) ◽  
pp. 17-32 ◽  
Author(s):  
BOSHENG SONG ◽  
TAO SONG ◽  
LINQIANG PAN

Tissue P systems are a class of bio-inspired computing models motivated by biochemical interactions between cells in a tissue-like arrangement. Tissue P systems with cell division offer a theoretical device to generate an exponentially growing structure in order to solve computationally hard problems efficiently with the assumption that there exists a global clock to mark the time for the system, the execution of each rule is completed in exactly one time unit. Actually, the execution time of different biochemical reactions in cells depends on many uncertain factors. In this work, with this biological inspiration, we remove the restriction on the execution time of each rule, and the computational efficiency of tissue P systems with cell division is investigated. Specifically, we solve subset sum problem by tissue P systems with cell division in a time-free manner in the sense that the correctness of the solution to the problem does not depend on the execution time of the involved rules.


2014 ◽  
Vol 568-570 ◽  
pp. 802-806
Author(s):  
Yun Yun Niu ◽  
Zhi Gao Wang

It is known that the Common Algorithmic Problem (CAP) has a nice property that several other NP-complete problems can be reduced to it in linear time. In the literature, the decision version of this problem can be efficiently solved with a family of recognizer P systems with active membranes with three electrical charges working in the maximally parallel way. We here work with a variant of P systems with active membranes that do not use polarizations and present a semi-uniform solution to CAP in the minimally parallel mode.


2018 ◽  
Vol 23 (12) ◽  
pp. 3903-3911 ◽  
Author(s):  
Suxia Jiang ◽  
Yanfeng Wang ◽  
Yansen Su

Author(s):  
Gheorghe Păun ◽  
Mario J. Perez-Jimenez ◽  
Agustín Riscos-Nunez

In tissue P systems several cells (elementary membranes) communicate through symport/antiport rules, thus carrying out a computation. We add to such systems the basic feature of (cell–like) P systems with active membranes – the possibility to divide cells. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time; we illustrate this possibility with SAT problem.


2015 ◽  
Vol 81 (2) ◽  
pp. 473-484 ◽  
Author(s):  
Petr Sosík ◽  
Luděk Cienciala
Keyword(s):  

Author(s):  
Majid Molki ◽  
Avinash Deshetty ◽  
Arun Rajendran

Turbulent flow in a rectangular duct with a plate blockage attached to the lower wall was numerically solved. The computational models used were Large Eddy Simulation (LES), 2D and 3D, steady and unsteady Reynolds-averaged Navier-Stokes (2D-SRANS and 3D-URANS). The fluid was air in all computations, and the Reynolds number (Re) was 5000 and 30,000. The predictions of LES were in several ways closer to the experimental data. For the two values of Re considered in this study, the LES over-predicted the location of maximum Nusselt number (Nu) by 24.1–24.9%, while the 3D-URANS under-predicted it by 23.7–36.8%. The best prediction for the value of maximum Nu was made by LES for Re = 5000, which was 9.3% higher than the experimental value. The LES under-predicted the maximum Nu by 24.1% for Re = 30,000. In the given range of Re, the under-predictions of 2D-SRANS and 3D-URANS for the value of maximum Nu were, respectively, 15.1–16.5% and 25.9–30.1%. As to the location of flow reattachment, the best value was predicted by the 2D-SRANS, while those of LES and 3D-URANS were close.


2016 ◽  
Vol 13 (7) ◽  
pp. 4293-4301 ◽  
Author(s):  
Wei Song ◽  
Ping Guo ◽  
HaiZhu Chen
Keyword(s):  

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