Solving Multidimensional 0-1 Knapsack Problem with Time-Free Tissue P Systems

Liu, Xiangrong; Li, Ziming; Suo, Juan Juan; Jin, Ying; Liu, Juan; Zeng, Xiangxiang

doi:10.1155/2014/372768

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…𝑃 systems have been proved to be a rich framework for handling many problems related to computing. Such systems can theoretically solve presumably intractable problems in a feasible time (solving NP-complete problems [10][11][12] or even PSPACE-complete problems [13]). Actually, 𝑃 systems can also provide some new ideas for designing optimization algorithms to obtain approximate solutions to the intractable problems [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Membrane Algorithms on GPU

Zhang

Wang

Ding

et al. 2014

Journal of Applied Mathematics

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Membrane algorithms are a new class of parallel algorithms, which attempt to incorporate some components of membrane computing models for designing efficient optimization algorithms, such as the structure of the models and the way of communication between cells. Although the importance of the parallelism of such algorithms has been well recognized, membrane algorithms were usually implemented on the serial computing device central processing unit (CPU), which makes the algorithms unable to work in an efficient way. In this work, we consider the implementation of membrane algorithms on the parallel computing device graphics processing unit (GPU). In such implementation, all cells of membrane algorithms can work simultaneously. Experimental results on two classical intractable problems, the point set matching problem and TSP, show that the GPU implementation of membrane algorithms is much more efficient than CPU implementation in terms of runtime, especially for solving problems with a high complexity.

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Section: Introductionmentioning

confidence: 99%

Implementation of Membrane Algorithms on GPU

Zhang

Wang

Ding

et al. 2014

Journal of Applied Mathematics

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Solving the 0-1 Knapsack Problem by Using Tissue P System With Cell Division

et al. 2019

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Delayed Spiking Neural P Systems with Scheduled Rules

Ren

Liu

2021

Complexity

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Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural-like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved.

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Solving Multidimensional 0-1 Knapsack Problem with Time-Free Tissue P Systems

Cited by 3 publications

References 31 publications

Implementation of Membrane Algorithms on GPU

Implementation of Membrane Algorithms on GPU

Solving the 0-1 Knapsack Problem by Using Tissue P System With Cell Division

Delayed Spiking Neural P Systems with Scheduled Rules

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